This document discusses the potential for machine learning to accelerate scientific discovery by rationalizing the inductive process of generating hypotheses from data. It outlines two approaches in science - theory/hypothesis-driven modeling and data-driven modeling using machine learning. It argues that machine learning can help "rationalize" the intuitive, non-logical parts of the scientific process by using data to generate and test hypotheses. The document also discusses how machine learning may automate parts of the scientific method, from hypothesis generation to model building and experimentation, thereby amplifying a scientist's progress.
An introductory workshop about machine learning in chemistry. This workshop is a set of slides and jupyter notebooks intended to give an overview of machine learning in chemistry to graduate students in chemical sciences, which was originally presented during a research trip to Ben Gurion University and the Hebrew University in Jerusalem in February 2019. Part 2 of 2.
The workshop lives at https://github.com/jpjanet/ML-chem-workshop where it is maintained in an up-to-date fashion. Notebook examples can be obtained from the GitHub page.
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
An introductory workshop about machine learning in chemistry. This workshop is a set of slides and jupyter notebooks intended to give an overview of machine learning in chemistry to graduate students in chemical sciences, which was originally presented during a research trip to Ben Gurion University and the Hebrew University in Jerusalem in February 2019. Part 2 of 2.
The workshop lives at https://github.com/jpjanet/ML-chem-workshop where it is maintained in an up-to-date fashion. Notebook examples can be obtained from the GitHub page.
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
Tutorial delivered at ECML-PKDD 2021.
TL;DR: This tutorial reviews recent developments on drug discovery using machine learning methods.
Powered by neural networks, modern machine learning has enjoyed great successes in data-intensive domains such as computer vision and languages where human can naturally perform well. Machine learning equipped with reasoning is now accelerating fields that traditionally require deep expertise such as physics, chemistry and biomedicine. This tutorial provides an overview of how machine learning and reasoning are speeding up and lowering the cost of drug discovery. This includes how machine learning can help in wide range of areas such as novel molecule identification, protein representation, drug-target binding, drug re-purposing, generative drug design, chemical reaction, retrosynthesis planning, drug-drug interaction, and safety assessment. We will also discuss relevant machine learning models for graph classification, molecular graph transformation, drug generation using deep generative models and reinforcement learning, and chemical reasoning.
"You can download this product from SlideTeam.net"
Keep your audience glued to their seats with professionally designed PPT slides. This deck comprises of total of fourtyseven slides. It has PPT templates with creative visuals and well researched content. Not just this, our PowerPoint professionals have crafted this deck with appropriate diagrams, layouts, icons, graphs, charts and more. This content ready presentation deck is fully editable. Just click the DOWNLOAD button below. Change the colour, text and font size. You can also modify the content as per your need. Get access to this well crafted complete deck presentation and leave your audience stunned. https://bit.ly/35jx0hU
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Molecular Mechanics in Molecular ModelingAkshay Kank
In this slide you learn about the computational chemistry and its role in designing a drug molecule. Also learn concept about the molecular mechanics and its application to Computer Aided Drug Design. difference between the Quantum mechanics and Molecular Mechanics.
Everything you want to know about role of artificial intelligence in drug discovery.
Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python,
deep neural network, GANs
AI in drug discovery and development
AI in clinical trials
"You can download this product from SlideTeam.net"
Keep your audience glued to their seats with professionally designed PPT slides. This deck comprises of total of fourtyseven slides. It has PPT templates with creative visuals and well researched content. Not just this, our PowerPoint professionals have crafted this deck with appropriate diagrams, layouts, icons, graphs, charts and more. This content ready presentation deck is fully editable. Just click the DOWNLOAD button below. Change the colour, text and font size. You can also modify the content as per your need. Get access to this well crafted complete deck presentation and leave your audience stunned. https://bit.ly/35jx0hU
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Molecular Mechanics in Molecular ModelingAkshay Kank
In this slide you learn about the computational chemistry and its role in designing a drug molecule. Also learn concept about the molecular mechanics and its application to Computer Aided Drug Design. difference between the Quantum mechanics and Molecular Mechanics.
Everything you want to know about role of artificial intelligence in drug discovery.
Artificial intelligence in health care and pharmacy, drug discovery, tensorflow, python,
deep neural network, GANs
AI in drug discovery and development
AI in clinical trials
The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
Future Directions in Chemical Engineering and BioengineeringIlya Klabukov
"Future Directions in Chemical Engineering and Bioengineering"
January 16-18, 2013
Austin, Texas
Chair: John G. Ekerdt, The University of Texas at Austin
Sponsored by Department of Defense,
Office of the Assistant Secretary of Defense for Research and Engineering
Chemical and biological engineers use math, physics, chemistry, and biology to develop chemical transformations and processes, creating useful products and materials that improve society. In recent years, the boundaries between chemical engineering and bioengineering have blurred as biology has become molecular science, more seamlessly connecting with the historic focus of chemical engineering on molecular interactions and transformations.
This disappearing boundary creates new opportunities for the next generation of engineered systems – hybrid systems that integrate the specificity of biology with chemical and material systems to enable novel applications in catalysis, biomaterials, electronic materials, and energy conversion materials.
Basic research for the U.S. Department of Defense covers a wide range of topics such as metamaterials and plasmonics, quantum information science, cognitive neuroscience, understanding human behavior, synthetic biology, and nanoscience and nanotechnology. Future Directions workshops such as this one identify opportunities
for continuing and future DOD investment. The intent is to create conditions for discovery and transformation, maximize the discovery potential, bring balance and coherence, and foster connections. Basic research stretches the limits of today’s technologies and discovers new phenomena and know-how that ultimately lead to future technologies and enable military and societal progress.
Philosophy of Biological Cell Repair informs Geoethical Nanotechnology: Cellular repair is an age-old function in biology. This talk examines the cellular process of repair in philosophical terms. Biologically, wound-healing is the primary form of cellular repair, drawing on numerous cell types and the extracellular matrix to perform a variety of operations during the phases of inflammation, proliferation, and maturation. Philosophically, these functions can be discussed from a systems theory perspective, through the concepts pairs of parts-whole, autonomy-dependency, self-other, sickness-wellness, and scarcity-abundance. Understanding cellular repair at the theory level could facilitate the development of nanotechnology solutions that augment biological processes in ways that are congruently geoethical with nature’s ethos.
Nature-inspired Solutions for Engineering: A Transformative Methodology for I...KTN
Nature- Inspired Engineering (NIE) is the application of fundamental scientific mechanisms, underpinning desirable properties observed in nature (e.g., resilience, scalability, efficiency), to inform the design of advanced technological solutions. As illustrated by the many applications, from energy technology, catalysis and reactor engineering, to functional materials for the built environment, electronic or optical devices, biomedical and healthcare engineering, NIE has the opportunity to inform transformative solutions to tackle some of our most pressing challenges, as well as to be a pathway to innovation.
The webcast recording is now available. Click here to watch it: https://www.youtube.com/watch?v=gPyTb_-qhgo
Find out more about the Nature Inspired Solutions special interest group at https://ktn-uk.co.uk/interests/nature-inspired-solutions
Join the Nature Inspired Solutions LinkedIn group at https://www.linkedin.com/groups/13701855/
In the present paper the experimental study of
Nanotechnology involves high cost for Lab set-up and the
experimentation processes were also slow. Attempt has also
been made to discuss the contributions towards the societal
change in the present convergence of Nano-systems and
information technologies. one cannot rely on experimental
nanotechnology alone. As such, the Computer- simulations and
modeling are one of the foundations of computational
nanotechnology. The computer modeling and simulations
were also referred as computational experimentations. The
accuracy of such Computational nano-technology based
experiment generally depends on the accuracy of the following
things: Intermolecular interaction, Numerical models and
Simulation schemes used. The essence of nanotechnology is
therefore size and control because of the diversity of
applications the plural term nanotechnology is preferred by
some nevertheless they all share the common feature of control
at the nanometer scale the latter focusing on the observation
and study of phenomena at the nanometer scale. In this paper,
a brief study of Computer-Simulation techniques as well as
some Experimental result
Applications of Computer Science in Environmental ModelsIJLT EMAS
Computation is now regarded as an equal and
indispensable partner, along with theory and experiment, in the
advance of scientific knowledge and engineering practice.
Numerical simulation enables the study of complex systems and
natural phenomena that would be too expensive or dangerous, or
even impossible, to study by direct experimentation. The quest
for ever higher levels of detail and realism in such simulations
requires enormous computational capacity, and has provided the
impetus for dramatic breakthroughs in computer algorithms and
architectures. Due to these advances, computational scientists
and engineers can now solve large-scale problems that were once
thought intractable. Computational science and engineering
(CSE) is a rapidly growing multidisciplinary area with
connections to the sciences, engineering, and mathematics and
computer science. CSE focuses on the development of problemsolving
methodologies and robust tools for the solution of
scientific and engineering problems. We believe that CSE will
play an important if not dominating role for the future of the
scientific discovery process and engineering design. The
computation science is now being used widely for environmental
engineering calculations. The behavior of environmental
engineering systems and processes can be studied with the help
of computation science and understanding as well as better
solutions to environmental engineering problems can be
obtained.
This review considers the application of CASE systems to a series of examples in which the original structures were later revised. We demonstrate how the chemical structure could be correctly elucidated if 2D NMR data were available and the expert system Structure Elucidator was employed. We will also demonstrate that if only 1D NMR spectra from the published articles were used then simply the empirical calculation of 13C chemical shifts for the hypothetical structures frequently enables a researcher to realize that the structural hypothesis is likely incorrect. We also analyze a number of erroneous structural suggestions made by highly qualified and skilled chemists. The investigation of these mistakes is very instructive and has facilitated a deeper understanding of the complicated logical-combinatorial process for deducing chemical structures.
Similar to Machine Learning for Chemical Sciences (20)
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneo...Ichigaku Takigawa
Video https://youtu.be/P4QogT8bdqY
ACS Spring 2023 Symposium on AI-Accelerated Scientific Workflow
https://acs.digitellinc.com/acs/sessions/526630/view
ACS SPRING 2023 ———— Crossroads of Chemistry
Indianapolis, IN & Hybrid, March 26-30
https://www.acs.org/meetings/acs-meetings/spring-2023.html
Slide PDF
https://itakigawa.page.link/acs2023spring
Our Paper
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (2022, ChemRxiv)
https://doi.org/10.26434/chemrxiv-2022-695rj
Ichi Takigawa
https://itakigawa.github.io/
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
Machine Learning for Molecular Graph Representations and GeometriesIchigaku Takigawa
Dec 1, 2021, Pacifico Yokohama, Japan.
Symposium 1AS-17 "Data science and machine learning: Tackling the Noise and Heterogeneity of the Real World"
The 44th Annual Meetingn of the Molecular Biology Society of Japan
https://www2.aeplan.co.jp/mbsj2021/english/designation/index.html
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.
Machine Learning for Chemical Sciences
1. Machine Learning for Chemical Sciences
Ichigaku Takigawa
HU-SNU Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
Graduate School of Information Science and Technology, HU
Institute for Chemical Reaction Design and Discovery (WPI-ICRD), HU
2. Brief Bio: Ichigaku Takigawa
Modulatory proteolysis
(MCP 2016)
Transcription elongation
regulation (Nat Comm 2015)
Neurodegenerative disease and
genomes (Mol Brain 2017)
Catalytic conversion of
methane (JPCC 2017)
10 years Hokkaido University
(1995-2005)
7 years at Kyoto University
(2005-2011)
7 years at Hokkaido University
(2012-2018)
PhD in Computer Science/Statistical Science
(Multivariate Analysis, Statistical Signal Processing)
• Bioinformatics Center, Institute for Chemical Research
• Graduate School of Pharmaceutical Sciences
(X-omics, Molecular Biology, Medicinal Chemistry, Biochemistry)
Research in Machine Learning & Data Mining
(ML with discrete structures, ML for sciences)
3. Two Approaches to Sciences
Our main interest in science: 'unknown mechanism/principle'
mechanism/principle
(unknown)
observations/data
Theory/Hypothesis-driven modeling (as in natural sciences)
explicit model observations/data
e.g. Newtonian mechanics, quantum mechanics, theory of relativity, etc.
we can run a simulation!
Data-driven modeling (as in statistics, machine learning, AI, etc)
model w/ unfixed observations/data
best tuned to fit the given data
✓
4. Deduction vs Induction/Abduction
The grand aim of science is to cover the greatest number
of experimental facts by logical deduction from the
smallest number of hypotheses or axioms.
Albert Einstein
Hypothesis Experimental facts (data)
Deduction
Induction
Abduction
“Experience/Observation”
“Intuition/Serendipity”
Not logical at all
Logical
Can we somehow “rationalize” this part by using the data?
5. Machine Learning for Sciences
REVIEW
Inverse molecular design using
machine learning: Generative models
for matter engineering
Benjamin Sanchez-Lengeling1
and Alán Aspuru-Guzik2,3,4
*
The discovery of new materials can bring enormous societal and technological progress. In this
context, exploring completely the large space of potential materials is computationally
intractable. Here, we review methods for achieving inverse design, which aims to discover
tailored materials from the starting point of a particular desired functionality. Recent advances
from the rapidly growing field of artificial intelligence, mostly from the subfield of machine
learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular
design are being proposed and employed at a rapid pace. Among these, deep generative models
have been applied to numerous classes of materials: rational design of prospective drugs,
synthetic routes to organic compounds, and optimization of photovoltaics and redox flow
batteries, as well as a variety of other solid-state materials.
M
any of the challenges of the 21st century
(1), from personalized health care to
energy production and storage, share a
common theme: materials are part of
the solution (2). In some cases, the solu-
tions to these challenges are fundamentally
limited by the physics and chemistry of a ma-
terial, such as the relationship of a materials
bandgap to the thermodynamic limits for the
generation of solar energy (3).
Several important materials discoveries arose
by chance or through a process of trial and error.
For example, vulcanized rubber was prepared in
the 19th century from random mixtures of com-
pounds, based on the observation that heating
with additives such as sulfur improved the
rubber’s durability. At the molecular level, in-
dividual polymer chains cross-linked, forming
bridges that enhanced the macroscopic mechan-
ical properties (4). Other notable examples in
this vein include Teflon, anesthesia, Vaseline,
Perkin’s mauve, and penicillin. Furthermore,
these materials come from common chemical
compounds found in nature. Potential drugs
either were prepared by synthesis in a chem-
ical laboratory or were isolated from plants,
soil bacteria, or fungus. For example, up until
2014, 49% of small-molecule cancer drugs were
natural products or their derivatives (5).
In the future, disruptive advances in the dis-
covery of matter could instead come from unex-
plored regions of the set of all possible molecular
and solid-state compounds, known as chemical
space (6, 7). One of the largest collections of
molecules, the chemical space project (8), has
mapped 166.4 billion molecules that contain at
most 17 heavy atoms. For pharmacologically rele-
vant small molecules, the number of structures is
estimated to be on the order of 1060
(9). Adding
consideration of the hierarchy of scale from sub-
nanometer to microscopic and mesoscopic fur-
ther complicates exploration of chemical space
in its entirety (10). Therefore, any global strategy
for covering this space might seem impossible.
Simulation offers one way of probing this
space without experimentation. The physics
and chemistry of these molecules are governed
by quantum mechanics, which can be solved via
the Schrödinger equation to arrive at their ex-
act properties. In practice, approximations are
used to lower computational time at the cost of
accuracy.
Although theory enjoys enormous progress,
now routinely modeling molecules, clusters, and
perfect as well as defect-laden periodic solids, the
size of chemical space is still overwhelming, and
smart navigation is required. For this purpose,
machine learning (ML), deep learning (DL), and
artificial intelligence (AI) have a potential role
to play because their computational strategies
automatically improve through experience (11).
In the context of materials, ML techniques are
often used for property prediction, seeking to
learn a function that maps a molecular material
to the property of choice. Deep generative models
are a special class of DL methods that seek to
model the underlying probability distribution of
both structure and property and relate them in a
nonlinear way. By exploiting patterns in massive
datasets, these models can distill average and
salient features that characterize molecules (12, 13).
Inverse design is a component of a more
complex materials discovery process. The time
scale for deployment of new technologies, from
discovery in a laboratory to a commercial pro-
duct, historically, is 15 to 20 years (14). The pro-
cess (Fig. 1) conventionally involves the following
steps: (i) generate a new or improved material
concept and simulate its potential suitability; (ii)
synthesize the material; (iii) incorporate the ma-
terial into a device or system; and (iv) characterize
and measure the desired properties. This cycle
generates feedback to repeat, improve, and re-
fine future cycles of discovery. Each step can take
up to several years.
In the era of matter engineering, scientists
seek to accelerate these cycles, reducing the
FRONTIERS IN COMPUTATION
KI
onJuly26,2018http://science.sciencemag.org/Downloadedfrom
REVIEW https://doi.org/10.1038/s41586-018-0337-2
Machine learning for molecular and
materials science
Keith T. Butler1
, Daniel W. Davies2
, Hugh Cartwright3
, Olexandr Isayev4
* & Aron Walsh5,6
*
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning
techniques that are suitable for addressing research questions in this domain, as well as future directions for the field.
We envisage a future in which the design, synthesis, characterization and application of molecules and materials is
accelerated by artificial intelligence.
T
he Schrödinger equation provides a powerful structure–
property relationship for molecules and materials. For a given
spatial arrangement of chemical elements, the distribution of
electrons and a wide range of physical responses can be described. The
development of quantum mechanics provided a rigorous theoretical
foundationforthechemicalbond.In1929,PaulDiracfamouslyproclaimed
that the underlying physical laws for the whole of chemistry are “completely
known”1
. John Pople, realizing the importance of rapidly developing
computer technologies, created a program—Gaussian 70—that could
perform ab initio calculations: predicting the behaviour, for molecules
of modest size, purely from the fundamental laws of physics2
. In the 1960s,
the Quantum Chemistry Program Exchange brought quantum chemistry
to the masses in the form of useful practical tools3
. Suddenly, experi-
mentalists with little or no theoretical training could perform quantum
calculations too. Using modern algorithms and supercomputers,
systems containing thousands of interacting ions and electrons can now
be described using approximations to the physical laws that govern the
world on the atomic scale4–6
.
The field of computational chemistry has become increasingly pre-
dictive in the twenty-first century, with activity in applications as wide
ranging as catalyst development for greenhouse gas conversion, materials
discovery for energy harvesting and storage, and computer-assisted drug
design7
. The modern chemical-simulation toolkit allows the properties
of a compound to be anticipated (with reasonable accuracy) before it has
been made in the laboratory. High-throughput computational screening
has become routine, giving scientists the ability to calculate the properties
of thousands of compounds as part of a single study. In particular, den-
sity functional theory (DFT)8,9
, now a mature technique for calculating
the structure and behaviour of solids10
, has enabled the development of
extensive databases that cover the calculated properties of known and
hypothetical systems, including organic and inorganic crystals, single
molecules and metal alloys11–13
.
The emergence of contemporary artificial-intelligence methods has
the potential to substantially alter and enhance the role of computers in
science and engineering. The combination of big data and artificial intel-
ligence has been referred to as both the “fourth paradigm of science”14
and the “fourth industrial revolution”15
, and the number of applications
in the chemical domain is growing at an astounding rate. A subfield of
generating, testing and refining scientific models. Such techniques are
suitable for addressing complex problems that involve massive combi-
natorial spaces or nonlinear processes, which conventional procedures
either cannot solve or can tackle only at great computational cost.
As the machinery for artificial intelligence and machine learning
matures, important advances are being made not only by those in main-
stream artificial-intelligence research, but also by experts in other fields
(domain experts) who adopt these approaches for their own purposes. As
we detail in Box 1, the resources and tools that facilitate the application
of machine-learning techniques mean that the barrier to entry is lower
than ever.
In the rest of this Review, we discuss progress in the application of
machine learning to address challenges in molecular and materials
research. We review the basics of machine-learning approaches, iden-
tify areas in which existing methods have the potential to accelerate
research and consider the developments that are required to enable more
wide-ranging impacts.
Nuts and bolts of machine learning
With machine learning, given enough data and a rule-discovery algo-
rithm, a computer has the ability to determine all known physical laws
(and potentially those that are currently unknown) without human
input. In traditional computational approaches, the computer is little
more than a calculator, employing a hard-coded algorithm provided
by a human expert. By contrast, machine-learning approaches learn
the rules that underlie a dataset by assessing a portion of that data
and building a model to make predictions. We consider the basic steps
involved in the construction of a model, as illustrated in Fig. 1; this
constitutes a blueprint of the generic workflow that is required for the
successful application of machine learning in a materials-discovery
process.
Data collection
Machine learning comprises models that learn from existing (train-
ing) data. Data may require initial preprocessing, during which miss-
ing or spurious elements are identified and handled. For example, the
Inorganic Crystal Structure Database (ICSD) currently contains more
than 190,000 entries, which have been checked for technical mistakes
DNA to be sequences into distinct pieces,
parcel out the detailed work of sequencing,
and then reassemble these independent ef-
forts at the end. It is not quite so simple in the
world of genome semantics.
Despite the differences between genome se-
quencing and genetic network discovery, there
are clear parallels that are illustrated in Table 1.
In genome sequencing, a physical map is useful
to provide scaffolding for assembling the fin-
ished sequence. In the case of a genetic regula-
tory network, a graphical model can play the
same role. A graphical model can represent a
high-level view of interconnectivity and help
isolate modules that can be studied indepen-
dently. Like contigs in a genomic sequencing
project, low-level functional models can ex-
plore the detailed behavior of a module of genes
in a manner that is consistent with the higher
level graphical model of the system. With stan-
dardized nomenclature and compatible model-
ing techniques, independent functional models
can be assembled into a complete model of the
cell under study.
To enable this process, there will need to
be standardized forms for model representa-
tion. At present, there are many different
modeling technologies in use, and although
models can be easily placed into a database,
they are not useful out of the context of their
specific modeling package. The need for a
standardized way of communicating compu-
tational descriptions of biological systems ex-
tends to the literature. Entire conferences
have been established to explore ways of
mining the biology literature to extract se-
mantic information in computational form.
Going forward, as a community we need
to come to consensus on how to represent
what we know about biology in computa-
tional form as well as in words. The key to
postgenomic biology will be the computa-
tional assembly of our collective knowl-
edge into a cohesive picture of cellular and
organism function. With such a comprehen-
sive model, we will be able to explore new
types of conservation between organisms
and make great strides toward new thera-
peutics that function on well-characterized
pathways.
References
1. S. K. Kim et al., Science 293 , 2087 (2001).
2. A. Hartemink et al., paper presented at the Pacific
Symposium on Biocomputing 2000, Oahu, Hawaii, 4
to 9 January 2000.
3. D. Pe’er et al., paper presented at the 9th Conference
on Intelligent Systems in Molecular Biology (ISMB),
Copenhagen, Denmark, 21 to 25 July 2001.
4. H. McAdams, A. Arkin, Proc. Natl. Acad. Sci. U.S.A.
94 , 814 ( 1997 ).
5. A. J. Hartemink, thesis, Massachusetts Institute of
Technology, Cambridge (2001).
V I E W P O I N T
Machine Learning for Science: State of the
Art and Future Prospects
Eric Mjolsness* and Dennis DeCoste
Recent advances in machine learning methods, along with successful
applications across a wide variety of fields such as planetary science and
bioinformatics, promise powerful new tools for practicing scientists. This
viewpoint highlights some useful characteristics of modern machine learn-
ing methods and their relevance to scientific applications. We conclude
with some speculations on near-term progress and promising directions.
Machine learning (ML) (1) is the study of
computer algorithms capable of learning to im-
prove their performance of a task on the basis of
their own previous experience. The field is
closely related to pattern recognition and statis-
tical inference. As an engineering field, ML has
become steadily more mathematical and more
successful in applications over the past 20
years. Learning approaches such as data clus-
tering, neural network classifiers, and nonlinear
regression have found surprisingly wide appli-
cation in the practice of engineering, business,
and science. A generalized version of the stan-
dard Hidden Markov Models of ML practice
have been used for ab initio prediction of gene
structures in genomic DNA (2). The predictions
correlate surprisingly well with subsequent
gene expression analysis (3). Postgenomic bi-
ology prominently features large-scale gene ex-
pression data analyzed by clustering methods
(4), a standard topic in unsupervised learning.
Many other examples can be given of learning
and pattern recognition applications in science.
Where will this trend lead? We believe it will
lead to appropriate, partial automation of every
element of scientific method, from hypothesis
generation to model construction to decisive
experimentation. Thus, ML has the potential to
amplify every aspect of a working scientist’s
progress to understanding. It will also, for better
or worse, endow intelligent computer systems
with some of the general analytic power of
creating hypotheses, testing by decisive exper-
iment or observation, and iteratively building
up comprehensive testable models or theories is
shared across disciplines. For each stage of this
abstracted scientific process, there are relevant
developments in ML, statistical inference, and
pattern recognition that will lead to semiauto-
matic support tools of unknown but potentially
broad applicability.
Increasingly, the early elements of scientific
method—observation and hypothesis genera-
tion—face high data volumes, high data acqui-
sition rates, or requirements for objective anal-
ysis that cannot be handled by human percep-
tion alone. This has been the situation in exper-
imental particle physics for decades. There
automatic pattern recognition for significant
events is well developed, including Hough
transforms, which are foundational in pattern
recognition. A recent example is event analysis
for Cherenkov detectors (8) used in neutrino
oscillation experiments. Microscope imagery in
cell biology, pathology, petrology, and other
Table 1. Parallels between genome sequencing
and genetic network discovery.
Genome
sequencing
Genome semantics
Physical maps Graphical model
Contigs Low-level functional
models
Contig
reassembly
Module assembly
Finished genome
sequence
Comprehensive model
C O M P U T E R S A N D S C I E N C E
onAugust29,2018http://science.sciencemag.org/Downloadedfrom
Nature, 559
pp. 547–555 (2018)
Science, 293
pp. 2051-2055 (2001)
Science, 361
pp. 360-365 (2018)
Science is changing, the tools of science are changing. And that
requires different approaches. Erich Bloch, 1925-2016
6. Machine Learning
Machine learning is a way to construct a computer program
directly by a given (large) collection of input-output examples
without being explicitly programmed.
Generic Object Recognition
Speech Recognition
Machine Translation
Super-Resolution Imaging
AI Game Players
“감사”
J’aime la
musique I love music
7. My Research Interest: ML with Discrete Structures
Tree/DAG Ensemble Neural Nets/Deep Learning Probabilistic Programming
• Target data themselves have discrete structures
• ML models have discrete structures or constraints
• Variables have structural dependencies or constraints
H
H
H
H
H
H
H
H
O
N
O
O
H
H
H
O
O
H
H
N
O
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Cl
ClCl
Sets, Sequences, Combinations, Permutations, Branching Chains (Trees), Networks (Graphs), …
8. Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
https://arxiv.org/abs/1703.00564
https://github.com/deepchem/deepchem (https://deepchem.io/)
CH3
N
H3C
H
NS
N
O
CH3
N
OH
x
molecule property value
ˆy
ˆy = f✓(x)
Quantitative structure–activity/property relationship (QSAR/QSPR)
9. Many levels of interest
• Mutagenic potency
• Carcinogenic potency
• Endocrine disruption
• Growth inhibition
• Aqueous solubility
N
NH
OO
HH
H
H H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
O
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O
O
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Cl
H
H
H
H
H
HH
H
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H
H
H
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Br
Br O P
O
O Br
Br
O
Br
Br
H
H
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HH
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HH
N
S
N
N
H
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Cl
ClCl
H
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H
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H
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H
H
H
N
CH3
O
O
H
N Cl
Cl
Cl
Cl
Cl
H3C
O O
O
O
O
O
H3C
CH3
CH2
O
HN
O
O
NH
CH3
HO
OH
CH3
N
O
O
CH3
N
N
H
N
H
H3C
N
H3C
H3C
NH
O
N
O
NO
CH3
O N
NH2
O
CH3
Br
CH3
N
H3C
H
NS
N
O
CH3
N
OH
CH3
CH3N
N
N
CH3H3C
H2N NH2
H
OH
O
HO
CH3
H
H
O
CH3
H
O
OH3C HH
H
O
H3C
S
CH3
O
H
H
O
CH3
CH3
OO
HO
H3CH
HO
F
H
O
H3C
NH2
O
N
HO
HO
O
H
H
O
O
OH3C
O
O
O
CH3
O
CH3
HO
CH3
H
O
O
CH3
H
H
N
H
N O
H3C
O
O
O
10. ML Surrogates for Quantum Chemistry
http://quantum-machine.org/datasets/
Quantum chemistry structures and properties of 134 kilo molecules, Scientific Data 1, 140022 (2014)
Dataset: structure-properties pairs in the ground state
for all 133,885 compounds with up to 9 heavy atoms of C, O, N, or F
DFT
B3LYP/6-31G(2df,p)
Structure in
the ground state
15 properties in
the ground state
http://www.nature.com/articles/sdata201422
11. Inverse Design (Machine Teaching)
“machine teaching”
“machine learning”
Bayesian optimization / Black-box optimization / Sequential
design of experiments / Surrogate-based optimization
“television”
Beware: Inverse is not unique??
CH3
N
H3C
H
NS
N
O
CH3
N
OH
x
molecule property value
ˆy
ˆy = f✓(x)
12. A lot of technical challenges, pitfalls, and potentials
14. ✗
ML for Heterogeneous Catalysis
Chemical Reaction
Catalysts
Heterogeneous catalysts / Surface reactions
Haber–Bosch Process
Ferrous metal Catalysis Noble Metal Catalysis (Pt, Pd, Rh…) Various Metallic Catalysts
(Li, rare earthes, alkaline earths)
(industrial synthesis of ammonia)
Exhaust Gas Purification Conversion of Methane
“Fertilizer from Air”
artificial nitrogen fixation
15. ML for Heterogeneous Catalysis
Catalyst Informatics
Large Literature Data
(in prep)
Quantum Calculated Data
(RCS Adv 2016; JPCC 2018)
Data from Controlled
Experiments (plan)
ACS 2018 [CATL]
Machine Learning for Catalysis Research
16. ML for Chemical Reaction Design & Discovery
Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), HU
We’ll start international hiring!!
Computational Science
Explores Quantum Chemically
Possible Reaction Paths
Information Science
Uses Various Data and Known
Information to Prioritize Paths
Experimental Science
Realizes & Validates Reactions in
Various Application Domains
The AFIR
method
2018 Oct 23-
HU 10 year project got funded
17. Machine Learning for Chemical Sciences
• Some words by David Hand at KDD2018@London:
• Theory-driven models can be wrong
• But data-driven models cannot be wrong or even right. 😩
• Data-driven are not trying to describe an underlying reality.
• But are merely intended to be useful. 😆
• So they could be poor or useless, but not wrong
• We need to understand the data, the models, the algorithms.
• We need to collaborate with domain experts because many
different conclusions are always possible from finite data.