AI is the Future of Drug Discovery
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Artificial intelligence (AI) has the potential to transform drug discovery through the use of semantic networks to represent biomedical knowledge as facts and infer new insights, probabilistic rules to evaluate beliefs about candidate drugs, and optimization and simulation techniques to iteratively improve outcomes. By continuously learning from diverse sources of data, AI systems could automate and enhance the processes of target identification, compound screening and testing in ways that accelerate research and development compared to traditional analytical tools.
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Artificial intelligence in drug discovery
This document discusses the use of artificial intelligence in drug discovery and development. It begins by defining artificial intelligence, machine learning, and deep learning. It then provides examples of how AI is currently used in various stages of the drug development process, including identifying molecular targets, finding hit compounds, optimizing lead compounds, predicting toxicity, and drug repurposing. It also discusses startups applying AI to drug discovery. Finally, it notes some limitations and drawbacks of using AI, such as potential bias in algorithms.
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Role of AI in Drug Discovery and Development
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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.
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3. Post-Market Monitoring.
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Sara Gerke: "AI in Drug Discovery and Clinical Trials"
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PLEASE NOTE: THESE SLIDES MAY NOT DISPLAY PROPERLY ONLINE, BUT THEY ARE READABLE IF DOWNLOADED.
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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.
Qsar lecture
QSAR attempts to quantify physicochemical properties like hydrophobicity, electronics, and steric effects and relate them to biological activity. Log P measures hydrophobicity and is often plotted against biological activity, sometimes giving a straight line and other times a parabolic curve. Other parameters like π, σ, and Es measure substituent hydrophobicity, electronic effects, and steric effects. QSAR equations combine these parameters to model and predict biological activity for a series of compounds.
Qsar
Quantitative structure-activity relationships (QSAR) use mathematical models to predict biological activity based on molecular properties. QSAR models are developed using statistical methods like partial least squares on datasets of compounds with known activities. Three-dimensional (3D) QSAR extends this approach by incorporating 3D structural descriptors and molecular fields derived from programs like CoMFA, VolSurf, and Catalyst to model activity based on interactions at binding sites. These 3D-QSAR models can be used to predict activity and design new compounds with improved properties.
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- Other stages of drug design discussed include lead identification, lead optimization, docking simulations to model drug-target binding, and pre-clinical trials to evaluate drug properties before human testing.
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Computer aided drug designing
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QSAR : Activity Relationships Quantitative Structure
A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
Qsar
The document discusses Quantitative Structure Activity Relationships (QSAR), which attempt to identify and quantify physicochemical properties of drugs that influence biological activity using mathematical equations. It describes key physicochemical properties considered in QSAR like hydrophobicity, electronic effects, and steric effects. Measurement scales for these properties like log P, π, σ, and Es are defined. The Hansch equation is presented as a typical QSAR model relating these factors to biological activity. Craig plots are also introduced to help select substituents for QSAR analysis.
Qsar and drug design ppt
The ppt describes Molecular descriptors and MAchine learning terms how its useful in chem informatics
Computer Aided Drug Design ppt
1) The document discusses the basics of drug design including defining the disease process, identifying targets for drug design like enzymes, receptors and nucleic acids, and the different approaches of ligand-based drug design and structure-based drug design.
2) It also covers important techniques in drug design like computer-aided drug design using computational methods, quantitative structure-activity relationships (QSAR), and the uses of computer graphics in molecular modeling and dynamics simulations.
3) Important experimental techniques discussed are x-ray crystallography and NMR spectroscopy that provide structural information for target biomolecules essential for structure-based drug design.
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SAR of Morphine
This document discusses structure-activity relationships in drug design and formulation. It introduces Hammett and Hansch plots, which relate reaction rates and biological activity to electronic and physicochemical properties. Modification of lead compounds is explored through changing functional groups, stereochemistry and lipophilicity. Morphine is used as a case study to illustrate how properties like log P, binding groups and stereochemistry impact opioid activity. The conclusion emphasizes the role of medicinal chemistry in understanding disease and developing safer, more effective pharmaceuticals.
Computer aided drug design
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AI is the Future of Drug Discovery
- 1. Artificial Intelligence is the Future of Drug Discovery David E Leahy Discovery Bus Ltd
- 2. • What is AI? • How do we build AI systems? • Why is this a good thing?
- 3. What is AI? • Software that makes decisions • Ambient • Independent • Declarative
- 4. Elements of AI Facts Rules Beliefs Inference Learning Optimise Simulate Sensors Decisions
- 5. Facts and Inference • mol1 binds protein1 • target involved disease1 • Protein subClass Target • protein1 similar protein2 • mol1 (binds (protein1 similar (protein2(Target(disease2))
- 6. The Semantic Web Bio2RDF (9 Billion Facts): OpenPhacts (3-4 million Facts)
- 7. Belief • mol binds protein • mol active assay • But • multiple measures • multiple predictions • Beliefs are probabilities, Facts are truths (see: Dempster-Shafer Theory for reasoning with uncertainty)
- 9. Belief Networks Structure and dynamics of molecular networks. Pharmacology & Therapeutics 138 (2013) 333–408. Peter Csermely et al Knowledge Vault. A web-scale approach to probabilistic knowledge fusion. K. Murphy et al,
- 10. Rules • Facts: When FactA & FactB & FactC then Decision • Beliefs: Costs, Benefits and Risks of Decision • Decisions • Molecule Facts -> Active • Research Target Profile (RTP) -> Candidate • Project Facts -> Invest
- 11. Search and Optimisation • Search World of Known Facts • Pass Rules? • Search World of Possible Facts • Move through World • Create new beliefs • Pass Rules?
- 12. Simulation • Dynamic simulations of protein structures (eg FEP) • Pharmacokinetic simulation from in vitro ADME • Efficacy and safety simulations
- 13. Elements of AI Facts Rules Beliefs Inference Learning Optimise Simulate Sensors Decisions
- 14. From Tools to AI • Always on, always learning • Generic processes & objective criteria • Multiple lines of evidence • Common Language
- 15. Why AI? • What would AI be like? • What would be different? • What would be better?