2.molecular modelling intro
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This document discusses the history and concepts of molecular modelling in drug design. It describes how early drug design involved trial and error methods to find biologically active molecules through random screening. The development of X-ray crystallography in the 1970s allowed visualization of 3D molecular structures, advancing drug design. Molecular modelling uses computer techniques based on chemistry and experimental data to analyze molecules and predict properties. The first generation of rational drug design used quantitative structure-activity relationships based on 2D structures. The second generation involves molecular modelling to simulate molecular interactions and design molecules meeting biological requirements through direct and indirect approaches as well as database searches and 3D computer-aided drug design.
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This document discusses molecular docking and different models used to describe molecular recognition between biomolecules. It begins by defining molecular recognition and docking, and describes early models like the lock-and-key and induced-fit models. It then discusses computational docking methods, including representing molecules, scoring docked poses, and search algorithms to generate poses. Flexibility is an important consideration, and methods to incorporate flexibility of both small molecule ligands and protein receptors are described.
10.637 Lecture 1: Introduction
This document contains notes from the first lecture of the MIT course 10.637 (quantum chemical simulation). The key points covered include:
- An introduction to atomistic and quantum chemical simulations and how they can provide insights into materials, catalysts, and chemical systems at the nanoscale.
- An overview of the course content, which will cover classical force fields, electronic structure theory, sampling methods, excited state methods and applications in various fields.
- Details on assignments, grading, and expectations upon completing the course.
- Case studies demonstrating different simulation techniques, including reaction discovery in nanoreactors, modeling protein-ligand binding, predicting singlet fission rates, and computational screening of surface catalysts.
Evolutionary Symbolic Discovery for Bioinformatics, Systems and Synthetic Bi...
The document discusses using evolutionary symbolic discovery methods to synthesize effective energy functions for protein structure prediction and systems/synthetic biology models. It describes using genetic programming techniques to explore large combinatorial spaces of modular components and parameters to construct stochastic P systems that model cellular systems. The goal is to find structures and optimize parameters in P systems to match target models through comparing different evolutionary algorithms on test cases of increasing difficulty and dimension.
Protdock - Aatu Kaapro
Molecular docking is used to predict how two molecular structures, such as a drug and protein, fit and bind together. It involves solving the 3D puzzle of their intermolecular binding. Docking software is mainly used in drug research to virtually screen compounds and identify potential inhibitors that bind to target proteins. The most important application is virtual screening to select promising molecules from databases for further study, requiring fast and reliable computational methods.
Programme epic1 v3
This document outlines the schedule and activities for the EPIC 1 school programme taking place from October 18-22, 2021. The programme includes introductory sessions on Git, data analysis with Python, and parallel sessions on advanced topics like image processing, astrophysics, materials science, and particle physics. On the final day, individual and group student presentations will be given on projects conducted during the programme. Presentation topics include data visualization in particle physics, characterization of cosmic ray fluxes in Ecuador, simulation of liquid crystal organization in human liver tissue, and simulation of electron scattering in dielectric materials.
Machine Learning for Molecules
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
chemoinformatics ppt 2.pptx
Chemoinformatics has developed over the past 50+ years to help analyze and utilize the large amount of chemical and biological data generated in drug discovery. Early work in the 1960s involved representing molecules as graphs to enable substructure searching of chemical databases. Key developments included the establishment of chemical literature databases and journals, as well as early computer systems for searching chemical structures and reactions. Methods from fields like artificial intelligence were also applied to problems like structure elucidation. Overall, chemoinformatics aims to transform chemical data into knowledge to help make better, faster decisions in drug lead identification and optimization.
defense7
The document summarizes Travis Hilbig's masters thesis defense on using molecular dynamics (MD) simulations to model the scratching of polymeric materials. The study aims to (1) create a model of high density polyethylene (HDPE), (2) simulate scratching on the material, (3) analyze penetration depth and recovery, and (4) determine factors influencing scratch behavior. MD simulations represent interactions using Lennard-Jones potentials parameterized for HDPE's intramolecular and intermolecular interactions. The results will provide insight into scratch resistance of polymers to optimize structures for applications.
A systematic approach for the generation and verification of structural hypot...
A systematic approach for the generation and verification of structural hypot...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
During the process of molecular structure elucidation the selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out using either empirical or quantum-mechanical (QM) methods. When QM methods are used, NMR prediction commonly utilizes the GIAO option of the DFT approximation. In this approach the structural hypotheses are expected to be investigated by scientist. In this article we hope to show that the most rational manner by which to create structural hypotheses is actually by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data and specifically using 2D NMR data. When an expert system is used the best structure(s) can be distinguished using chemical shift prediction, which is best performed either by an incremental or neural net algorithm. The time-consuming QM calculations can then be applied, if necessary, to one or more of the 'best' structures to confirm the suggested solution.5. STRUCTURE BASED DD.pptx
This document discusses structure-based drug discovery and design. It begins with definitions of key terms like drug, receptor, pharmacophore, and stereochemistry. It then covers topics like the Cahn-Ingold-Prelog system for naming stereoisomers, examples of enantiomers and diastereomers, and how stereochemistry impacts biological activity. The document also discusses computer-aided drug design approaches like quantitative structure-activity relationships, molecular docking, X-ray crystallography, NMR, and homology modeling. It explains the goals and techniques of drug design, including ligand-based and structure-based methods.
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The interplay between data-driven and theory-driven methods for chemical scie...
The interplay between data-driven and theory-driven methods for chemical scie...
Report: "MolGAN: An implicit generative model for small molecular graphs"
Report: "MolGAN: An implicit generative model for small molecular graphs"
Evolutionary Symbolic Discovery for Bioinformatics, Systems and Synthetic Bi...
Evolutionary Symbolic Discovery for Bioinformatics, Systems and Synthetic Bi...
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More from Abhijeet Kadam
27.docking protein-protein and protein-ligand
This document discusses protein-protein and protein-ligand docking. It begins by defining docking as determining whether two biological molecules interact and finding their lowest energy orientation if so. The document then discusses challenges like the large number of possible conformations and small energy changes. It describes different docking study types and techniques used, including surface representation methods and algorithms like DOCK and RosettaDOCK. Finally, it summarizes a protein-protein docking algorithm and notes current problems in docking relate to limited flexibility handling and scoring function efficiency.
26.docking
This document discusses virtual screening and molecular docking techniques used in drug discovery. It notes that virtual screening involves creating a large virtual library that is screened using docking programs to select the most active compounds for laboratory testing. Molecular docking requires knowledge of the 3D structure of the target protein binding site and involves placing flexible ligand molecules into the target's active site to investigate binding interactions and detect new binding pockets in a cost-effective and time-saving approach compared to laboratory high-throughput screening.
25.qsar
This document provides an overview of QSAR (Quantitative Structure-Activity Relationship) modeling, which establishes mathematical relationships between chemical structure descriptors of molecules and their biological activity. It discusses how QSAR analysis involves statistically analyzing properties of known active molecules to develop models that can then predict activity of new compounds. Examples of using QSAR to optimize ligand structures and select candidates for further testing are also provided.
24.prodrug
A prodrug is a precursor compound that is metabolized within the body to form an active drug. Prodrugs can improve how medicines are absorbed, distributed, metabolized, and eliminated to reduce unintended side effects. There are two types of prodrugs - Type I are activated intracellularly by enzymes, while Type II are activated extracellularly by enzymes or in bodily fluids. Examples of prodrugs include heroin metabolites and diet pills. Endogenous compounds are substances naturally found in or produced by the body that can act as drugs or help other compounds act as drugs. Determining the effects of administering endogenous compound drugs can be difficult due to existing background levels in the body.
23.optimizing access to target
This document discusses strategies for drug design optimization. It outlines seven aims of drug design including selectivity for the target, activity level, minimal side effects, ease of synthesis, chemical stability, acceptable pharmacokinetic properties, and non-toxicity. It also discusses two important aspects of drug design: pharmacodynamics to improve ability to reach the target, and pharmacokinetics to have an acceptable lifetime. Various strategies are presented to optimize absorption, metabolism, and excretion of drugs in order to efficiently deliver them to their targets.
22.pharmacophore
This document discusses the identification and generation of pharmacophores. A pharmacophore is a specific 3D arrangement of functional groups within a molecule that are necessary for binding to an enzyme or receptor. Pharmacophore identification is important for understanding ligand-receptor interactions. Pharmacophore models are derived from the common features of known active molecules and define the spatial relationships between these features. Several computational methods can be used to generate pharmacophore hypotheses, including systematic search, distance geometry, and clique detection algorithms.
21.umls
The UMLS (Unified Medical Language System) is a set of files and software owned by the NIH that brings together many health and biomedical vocabularies to enable interoperability between computer systems. It allows linking of terms and codes across different systems like doctors, pharmacies, and insurance. The UMLS has three main components: the Metathesaurus containing terms and codes from many vocabularies, the Semantic Network organizing terms into categories and relationships, and lexical tools for natural language processing.
20.drug discovery
Computer aided drug design (CADD) involves 5 steps: new lead discovery, lead optimization, preclinical lead development, clinical development, and post marketing surveillance. New lead discovery involves finding relevant enzymes/receptors through assays, purifying and characterizing them, determining structures, and screening known and designed compounds. Lead optimization develops secondary assays, tests related structures, and analyzes structure-activity relationships and properties. Preclinical development focuses on large scale synthesis, animal safety studies, and formulation experiments. Clinical development includes human safety studies and clinical trials. Post marketing surveillance collects usage and side effect reports. Strategies for new leads include isolating from nature, modifying natural products, screening known substances, and structure-based design.
19.cobra
COBRA (Common Object Request Broker Architecture) is a standard that allows programs written in different programming languages and running on different machines to communicate and interoperate. It defines how objects can be requested and used across a network. The key component of COBRA is the Object Request Broker (ORB) which acts as a middleware that allows client programs to communicate with server objects without needing to know the object's location, programming language, or platform. This allows new objects to communicate with existing ones as long as they follow the standard interface definitions.
18.xml
This document discusses XML (eXtensible Markup Language). It describes how XML is used to store and exchange data using user-defined tags. It can embed data within HTML code to display in browsers. XML has flexibility and is widely used in bioinformatics databases and services like NCBI, EMBL, DDBJ, and Gene Ontology. An example XML document is provided to showcase its structure with tags, elements, attributes, and proper nesting.
17.interoperability
Interoperability refers to the ability of different systems and organizations to work together and exchange information. There are three main types: syntactic interoperability which ensures data can be communicated and exchanged using common formats and protocols; semantic interoperability which allows the automatic interpretation of exchanged information so that both systems can produce useful results; and cross-domain interoperability which involves multiple different entities collaborating for shared interests and information exchange.
15.simulated anneling
Simulated annealing is an optimization algorithm inspired by metallurgy. It combines Markov chain Monte Carlo and the Metropolis algorithm to find optimal solutions to problems by mimicking the physical process of annealing. The algorithm starts at a high temperature and progressively lowers it, simulating the cooling of metals in annealing. This allows the algorithm to avoid being trapped in local minima and find the global minimum, allowing for problems to be solved that cannot be solved using traditional optimization techniques alone.
14.machine learning
Machine learning and data mining are closely related. Data mining involves analyzing data to discover patterns and extract knowledge. Common techniques for data mining include association, sequence analysis, classification, clustering, and forecasting. Data mining can be predictive, descriptive, or both. Most data mining methods use statistical methods and are sophisticated enough to be considered machine learning. Machine learning involves programs that can adapt based on experience. Machine learning can be supervised, using labeled training data, or unsupervised, to discover patterns without labels. Common machine learning methods include hidden Markov models, clustering, decision trees, neural networks, genetic algorithms, support vector machines, and simulated annealing.
12.protein folding
Protein folding is a complex multi-step process whereby a protein folds from a random coil into its functional three-dimensional structure. It begins with the rapid formation of alpha helices and beta sheets within milliseconds. These initial elements then interact and collapse into a molten globule structure driven by hydrophobic interactions. Over the next few seconds, the protein undergoes rearrangements to attain its stable tertiary structure and expel water from its hydrophobic core. The folding process is directed by the interior residues and proceeds through a folding funnel from a high-energy random state to the native low-energy conformation.
11.ramachandran plot
A Ramachandran plot visualizes the backbone of amino acid residues by plotting the possible phi and psi dihedral angles, which were originally developed by Ramachandran et al. in 1963. It shows favored, allowed, and generously allowed regions based on analysis of protein crystal structures. Two high density regions correspond to alpha helices and beta sheets. Ramachandran plots are used for protein structure validation and show amino acid preferences and distributions observed in protein data bank structures.
10.torsion angles
Torsion angles describe the rotation of planes or groups of atoms. For proteins, important torsion angles include phi (φ), psi (ψ), and omega (ω) which describe the rotation of the protein backbone. The Ramachandran plot maps φ and ψ angles and shows allowed and disallowed regions, providing a quality check for protein structures. Torsion angles are critical for determining protein folding as they provide flexibility for the backbone to adopt certain folds.
9.protein ligand interactions2
Protein-ligand interactions are important for enzyme function, receptor actions, and cellular structure formation. Proteins selectively bind to other molecules through spontaneous processes driven by similar forces as protein folding. A protein's function is defined by its interactions with other ligands, which can occur at one or more binding sites. Cooperative effects can influence how additional ligands bind, either positively or negatively. Multivalent interactions between multiple weak binding points on a ligand and protein can also result in high affinity binding. Computational methods can be used to study and model these protein-ligand interactions.
8.protein ligand interactions
Protein ligands bind to specific sites on proteins. Common protein ligands include antibodies and molecules like nucleic acids and peptides. Main methods to study protein-ligand interactions are spectroscopic techniques like fluorescence spectroscopy and structural methods like X-ray crystallography and NMR spectroscopy. Protein-ligand interactions are crucial for processes in living organisms as they allow for molecular recognition and signal transmission essential to biological functions.
6.ab initio qm
The document discusses quantum mechanics and ab initio methods. It explains that the Schrodinger equation is the basic wave equation used in quantum mechanics to describe particle behavior. Ab initio methods refer to solving the Schrodinger equation without approximations, which is the highest level of quantum mechanics. There are two approaches to ab initio - calibrated uses a fixed basis set and calibrates calculations, while converged uses improving basis sets until results converge. Ab initio is based on the self-consistent field method and Hartree-Fock approximation to calculate energies and new orbitals iteratively until self-consistency is achieved.
5.quantum mechanics
Semiempirical calculations use approximations of quantum mechanics and experimental data to calculate molecular properties through iterative computations. These methods treat orbitals as linear combinations that are optimized in a self-consistent field to minimize energy. Improved semiempirical methods like MINDO, AMI, and MOPAC are parameterized using experimental data for certain elements and allow calculations of molecular geometry, energy, orbital shapes, charges, and vibrational properties through the MOPAC software package.
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留信网认证的作用:
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3:国家专业人才认证中心颁发入库证书
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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The binding of cosmological structures by massless topological defects
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
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Phenomics assisted breeding in crop improvement
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Authoring a personal GPT for your research and practice: How we created the Q...
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
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2.molecular modelling intro
- 2. 2 Pre Bioinformatics Era ● Take a lead molecule ● Develop a chemical program ● Find analog molecules exibiting desired biological properties ● Hope to find a molecule of use by 'chance observation or 'random screening' ● This involved trial & error method ● In-vitro + In Vivo ● Expensive ● Ineffecient Yet we have so many drugs available today.
- 3. 3 Pre Bioinformatics Era ● Fischer (1894) & Ehrlich (1609) introduces the concept of 'Lock & Key' model ● 1970's – X-ray crystallography developed ● Hence the 3D structures of molecules ● And advancements in Drug Design
- 4. 4 Concept of MM ● New molecules conceived on basis of similarity with known reference structure or ● On basis of the comlementarity of 3D structure of Known molecules
- 5. 5 Concept of MM ● Molecular interactions are fixed ● Hence, MM is a discipline that contributes to the understanding of these processes in qualitative and quantative manner. ● Computerized techniques – based on chemistry methods and expt. Data ● Used to analyze – molecules and moleculars systems or ● to predict molecules and biological properties
- 6. 6 1st Generation of Rational Approach in DD ● Rational DD – based on concept that biological properties of molecules are related to their structural features ● In 1970's – 2D structures were considered to be final. ● Then came QSAR – quantative str-activity relationship ● This became famous ● Got implemented in computers ● Which gave us 1st Gen CADD ● By Hansch and group (fujita, 1990)
- 7. 7 ● Based on – ➔ Mathametical equations ➔ expressing biological activity ➔ in terms of molecular parameters ➔ Such as log p (partition coefficient) ➔ Es (Steric constant) ➔ MR (Molar refractivity) etc... ● But this proved helpful only for optimization based on 2D formula and fixed 2D frame
- 8. 8 2nd Generation : Molecular Modelling A) Conceptual Frame B) Fields covered
- 9. 9 Conceptual Frame ● Uses DD concept of lock and key. It works. ● Here imagination and intution is very important ● You have to imagine or visualize a molecular structure ● Molecular modelling – Small molecules ● MM of sets of small molecules – Macromolecules ● Ligand-receptor fir analysis ● Direct + Indirect – Molecule design confirming requirements
- 10. 10 Fields Covered 1) Direct DD – Expt.proven date (X-ray str.) 2) Indirect DD – Stereochemical and physciochemical features lead designed by pharmacophore 3) Database searches – search online wrt pharmacophore 4) 3D – CADD – generated by computers (macheine learning) 5) Molecular Mimicry – mimics of known reference compound