Vigyaan is a live Linux environment based on Knoppix that provides bioinformatics tools for tasks like sequence alignment, structure visualization, molecular dynamics simulations, and more. It contains demos of tools like Artemis, ClustalX, Ghemical, GROMACS, Jmol, Open Babel, NJPlot, PSI3, PyMOL, Rasmol, Raster3D, and TINKER. PSI3 is a program for ab initio quantum chemistry calculations that can perform Hartree-Fock, coupled cluster, and configuration interaction methods with basis sets of up to 32,768 functions.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
Protein docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There are both high specificity and induced fit within these interfaces with specificity increasing with rigidity. The foremost thing that we need to start with a docking search is the sequence of our protein of interest. (Halperin et al., 2002).
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tends to be flatter and smoother than those in interfaces of these interactions do not have the ability to alter protein-ligand interactions. Protein-protein interactions are usually more rigid, the conformation in order to improve binding and ease movement. (Smith and Sternberg, 2002).
The process of drug development has revolved around a screening approach, as nobody knows which compound or approach could serve as a drug or therapy. Such almost blind screening approach is very time-consuming and laborious. The goal of structure-based drug design is to find chemical structures fitting in the binding pocket of the receptor. Based on the three-dimensional structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them (Weil et al., 2004).
A homology model of the housefly voltage-gated sodium channel was developed to predict the location of binding sites for the insecticides fenvalerate, a synthetic pyrethroid, and DDT, an early generation organochlorine. The model successfully addresses the state-dependent affinity of pyrethroid insecticides. (O’Reilly et al., 2006).
Interaction fingerprint: 1D representation of 3D protein-ligand complexesVladimir Chupakhin
Structural interaction fingerprint1 (IFP) was introduced in order to overcome the shortcomings of the existing scoring functions. IFP represent a binary string that encoding a presence or an absence of interactions of a ligand with amino acids of a protein binding site. It is a convenient way to compare and analyze binding poses of the ligands.
A lecture on molecular docking that I give for master students at University Paris Diderot.
Warning: this presentation has numerous animations which are not included in the slideshare document.
https://florentbarbault.wordpress.com/
ADMET properties prediction using AI will accelerate the process of drug discovery.
This slide mostly focuses on using graph-based deep learning techniques to predict drug properties.
A short and quick presentation on predicting ligand binding sites which include four methods for prediction binding sites.First go through the references and then quick look at this presentation really helpful to you. Enjoy
This event featured an update from the Presidential Commission for the Study of Bioethical Issues delivered by Michelle Groman (HLS '05), Associate Director at the Bioethics Commission. Since its inception in 2009, President Obama's Commission has issued reports on synthetic biology, human subjects research, whole genome sequencing, pediatric medical countermeasure research, and incidental findings. Currently, the Commission is examining the ethical implications of neuroscience research and the application of neuroscience research findings as part of the federal government’s BRAIN Initiative. The Commission also has developed educational materials to support teaching of bioethics ideas, principles, and theories in traditional and non-traditional settings.
The final half-hour of the event featured a discussion of career opportunities in law and bioethics, led by Ms. Groman and Holly Fernandez Lynch, Petrie-Flom Center Executive Director.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
Protein docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There are both high specificity and induced fit within these interfaces with specificity increasing with rigidity. The foremost thing that we need to start with a docking search is the sequence of our protein of interest. (Halperin et al., 2002).
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tends to be flatter and smoother than those in interfaces of these interactions do not have the ability to alter protein-ligand interactions. Protein-protein interactions are usually more rigid, the conformation in order to improve binding and ease movement. (Smith and Sternberg, 2002).
The process of drug development has revolved around a screening approach, as nobody knows which compound or approach could serve as a drug or therapy. Such almost blind screening approach is very time-consuming and laborious. The goal of structure-based drug design is to find chemical structures fitting in the binding pocket of the receptor. Based on the three-dimensional structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them (Weil et al., 2004).
A homology model of the housefly voltage-gated sodium channel was developed to predict the location of binding sites for the insecticides fenvalerate, a synthetic pyrethroid, and DDT, an early generation organochlorine. The model successfully addresses the state-dependent affinity of pyrethroid insecticides. (O’Reilly et al., 2006).
Interaction fingerprint: 1D representation of 3D protein-ligand complexesVladimir Chupakhin
Structural interaction fingerprint1 (IFP) was introduced in order to overcome the shortcomings of the existing scoring functions. IFP represent a binary string that encoding a presence or an absence of interactions of a ligand with amino acids of a protein binding site. It is a convenient way to compare and analyze binding poses of the ligands.
A lecture on molecular docking that I give for master students at University Paris Diderot.
Warning: this presentation has numerous animations which are not included in the slideshare document.
https://florentbarbault.wordpress.com/
ADMET properties prediction using AI will accelerate the process of drug discovery.
This slide mostly focuses on using graph-based deep learning techniques to predict drug properties.
A short and quick presentation on predicting ligand binding sites which include four methods for prediction binding sites.First go through the references and then quick look at this presentation really helpful to you. Enjoy
This event featured an update from the Presidential Commission for the Study of Bioethical Issues delivered by Michelle Groman (HLS '05), Associate Director at the Bioethics Commission. Since its inception in 2009, President Obama's Commission has issued reports on synthetic biology, human subjects research, whole genome sequencing, pediatric medical countermeasure research, and incidental findings. Currently, the Commission is examining the ethical implications of neuroscience research and the application of neuroscience research findings as part of the federal government’s BRAIN Initiative. The Commission also has developed educational materials to support teaching of bioethics ideas, principles, and theories in traditional and non-traditional settings.
The final half-hour of the event featured a discussion of career opportunities in law and bioethics, led by Ms. Groman and Holly Fernandez Lynch, Petrie-Flom Center Executive Director.
My introduction to electron correlation is based on multideterminant methods. I introduce the electron-electron cusp condition, configuration interaction, complete active space self consistent field (CASSCF), and just a little information about perturbation theories. These slides were part of a workshop I organized in 2014 at the University of Pittsburgh and for a guest lecture in a Chemical Engineering course at Pitt.
Associate Professor Regine Wagner's workshop slides. Workshop to support the FLI, CSU (Aus) & Massey (NZ) research project “Fostering institutional change through distributive leadership approaches: Engaging academics and teaching support staff in blended and flexible learning”is being conducted as a partnership between CSU and Massey Universities.
The research methodology includes a force field analysis as a mechanism for analysing and describing the driving and constraining forces that shape the project at international, national, and local institutional levels.
Autodock Made Easy with MGL Tools - Molecular DockingGirinath Pillai
Restructured tutorial for AutoDock and AutoGrid with MGL Tools. Prepared during 2011 adapted from original AutoDock MGL Tools Tutorial
and a video tutorial with the latest enhancements and options are uploaded to Youtube: https://www.youtube.com/watch?v=n53gJE8SHOM
A PROGRESSIVE MESH METHOD FOR PHYSICAL SIMULATIONS USING LATTICE BOLTZMANN ME...ijdpsjournal
In this paper, a new progressive mesh algorithm is introduced in order to perform fast physical simulations by the use of a lattice Boltzmann method (LBM) on a single-node multi-GPU architecture. This algorithm is able to mesh automatically the simulation domain according to the propagation of fluids. This method can also be useful in order to perform several types of physical simulations. In this paper, we associate this
algorithm with a multiphase and multicomponent lattice Boltzmann model (MPMC–LBM) because it is
able to perform various types of simulations on complex geometries. The use of this algorithm combined
with the massive parallelism of GPUs[5] allows to obtain very good performance in comparison with the
staticmesh method used in literature. Several simulations are shown in order to evaluate the algorithm.
PROBABILISTIC DIFFUSION IN RANDOM NETWORK G...ijfcstjournal
In this paper, we consider a random network such that there could be a link between any two nodes in the network with a certain probability (plink). Diffusion is the phenomenon of spreading information throughout the network, starting from one or more initial set of nodes (called the early adopters). Information spreads along the links with a certain probability (pdiff). Diffusion happens in rounds with the first round involving the early adopters. The nodes that receive the information for the first time are said to be covered and
become candidates for diffusion in the subsequent round. Diffusion continues until all the nodes in the network have received the information (successful diffusion) or there are no more candidate nodes to spread the information but one or more nodes are yet to receive the information (diffusion failure). On the basis of exhaustive simulations conducted in this paper, we observe that for a given plink and pdiff values, the fraction of successful diffusion attempts does not appreciably change with increase in the number of early
adopters; whereas, the average number of rounds per successful diffusion attempt decreases with increase
in the number of early adopters. The invariant nature of the fraction of successful diffusion attempts with increase in the number of early adopters for a random network (for fixed plink and pdiff values) is an interesting and noteworthy observation (for further research) and it has not been hitherto reported in the literature.
Machine Learning (ML) models are often composed as pipelines of operators, from “classical” ML operators to pre-processing and featurization operators. Current systems deploy pipelines as "black boxes”, where the same implementation of training is run for inference. This solution is convenient but leaves large room to improve performance and resource usage. This talk presents Pretzel, a framework for deployment of ML pipelines that is inspired to Database Systems: Pretzel inspects and optimizes pipelines end-to-end much like queries, and manages resources common to multiple pipelines such as operators' state. Pretzel is joint work with University of Seoul and Microsoft Research and has recently been presented at OSDI ’18. After the overview, this talk also shows experimental results of Pretzel against state-of-art ML solutions and discusses limitations and extensions.
Problems in Task Scheduling in Multiprocessor Systemijtsrd
This Contemporary computer systems are multiprocessor or multicomputer machines. Their efficiency depends on good methods of administering the executed works. Fast processing of a parallel application is possible only when its parts are appropriately ordered in time and space. This calls for efficient scheduling policies in parallel computer systems. In this work deterministic problems of scheduling are considered. The classical scheduling theory assumed that the application in any moment of time is executed by only one processor. This assumption has been weakened recently, especially in the context of parallel and distributed computer systems. This monograph is devoted to problems of deterministic scheduling applications (or tasks according to the scheduling terminology) requiring more than one processor simultaneously. We name such applications multiprocessor tasks. In this work the complexity of open multiprocessor task scheduling problems has been established. Algorithms for scheduling multiprocessor tasks on parallel and dedicated processors are proposed. For a special case of applications with regular structure which allow for dividing it into parts of arbitrary size processed independently in parallel, a method of finding optimal scattering of work in a distributed computer system is proposed. The applications with such regular characteristics are called divisible tasks. The concept of a divisible task enables creation of tractable computation models in a wide class of computer architectures such as chains, stars, meshes, hypercubes, multistage networks. Divisible task method gives rise to the evaluation of computer system performance. Examples of such performance evaluation are presented. This work summarizes earlier works of the author as well as contains new original results. Mukul Varshney | Jyotsna | Abhakiran Rajpoot | Shivani Garg"Problems in Task Scheduling in Multiprocessor System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2198.pdf http://www.ijtsrd.com/computer-science/computer-architecture/2198/problems-in-task-scheduling-in-multiprocessor-system/mukul-varshney
Many Machine Learning inference workloads compute predictions based on a limited number of models that are deployed together in the system. These models often share common structure and state. This scenario provides large rooms for optimizations of runtime and memory, which current systems fall short in exploring because they employ a black-box model of ML models and tasks, thus being unaware of optimization and sharing opportunities.
On the opposite side, Pretzel adopts a white-box description of ML models, which allows the framework to perform optimizations over deployed models and running tasks, saving memory and increasing the overall system performance. In this talk we will show the motivations behind Pretzel, its current design and possible future developments.
Slides 23 and 24 mentions experience with HDF-EOS.
Source: http://hdfeos.org/workshops/ws04/presentations/Jones/000901%20DPEAS%20Overview%20-%20HDFEOS%20Workshop.ppt
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
14. molecular dynamics (MD) a computer simulation technique where the time evolution of a set of interacting atoms is followed by integrating their equations of motion In molecular dynamics we follow the laws of classical mechanics, and most notably Newton's law: F=ma Here, m is the atom mass, a its acceleration, and F the force acting upon it, due to the interactions with other atoms.
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22. PSI3 is a program system and development platform for ab initio molecular electronic structure computations. The PSI3 suite of quantum chemical programs is designed for efficient, high-accuracy calculations of properties of small to medium-sized molecules. It’s capabilities include a variety of Hartree-Fock, coupled cluster, complete-active-space self-consistent-field, and multi-reference configuration interaction models. Molecular point-group symmetry is utilized throughout to maximize efficiency. Non-standard computations are possible using a customizable input format. PSI3 can perform ab initio computations employing basis sets of up to 32768 contracted Gaussian-type functions of virtually arbitrary orbital quantum number. PSI3 can recognize and exploit the largest Abelian subgroup of the point group describing the full symmetry of the molecule.
23. It includes mature programming interfaces for parsing user input, accessing commonly used data such as basis-set information or molecular orbital coefficients, and retrieving and storing binary data especially multi-index quantities such as electron repulsion integrals. This platform is useful for the rapid implementation of both standard quantum chemical methods, as well as the development of new models. Features that have already been implemented include Hartree-Fock, multiconfigurational self-consistent-field, second-order Møller-Plesset perturbation theory, coupled cluster, and configuration interaction wave functions. Distinctive capabilities include the ability to employ Gaussian basis functions with arbitrary angular momentum levels; linear R12 second-order perturbation theory; coupled cluster frequency-dependent response properties, including dipole polarizabilities and optical rotation; and diagonal Born-Oppenheimer corrections with correlated wave functions.
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