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Machine Learning in Chemistry and Drug Candidate Selection

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Girinath Pillai
Girinath Pillai

Application of machine learning and its importance in chemistry, drug discovery, materials science and requirement of the right dataset of chemical structures and activities. Drug Candidate selection criteria is important to avoid failures

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@giribio Girinath G. Pillai, PhD @giribio Machine Learning in Drug Discovery for Drug Candidate Selection @giribio Girinath G. Pillai, PhD 1
@giribio Girinath G. Pillai, PhD @giribio ● We are not yet completely ready with AI/ML in Drug Discovery (it takes time like Human Genome Project) ● Slides contains contents/pictures/videos taken from web, articles, lectures, tutorials and its respective authors own their copyrights. Technical Slides : slideshare.net/giribio Case Studies : youtube.com/giribio Workflows & Notebooks : github.com/giribio NOTE 2
@giribio Girinath G. Pillai, PhD @giribio National Employability 3
@giribio Girinath G. Pillai, PhD @giribio Bridge the Gap 4
@giribio Girinath G. Pillai, PhD @giribio Industry 4.0 5
@giribio Girinath G. Pillai, PhD @giribio AGENDA 01 AI & Machine Learning What? Why? How? 03 Drug Discovery How to avoid failures? 02 ML in Chemistry Chemical data in DS 04 What to do Next? Are you ready for AI/ML? 6
@giribio Girinath G. Pillai, PhD @giribio 01 AI & Machine Learning What? Why? How? 7
@giribio Girinath G. Pillai, PhD @giribio “Learning denotes changes in a system that ... enable a system to do the same task more efficiently the next time” —Herbert Simon 8
@giribio Girinath G. Pillai, PhD @giribio ● Understand and improve efficiency of human learning ○ Improve methods for teaching and tutoring people (better CAI) ● Discover new things or structure that were previously unknown to humans ○ Examples: data mining, scientific discovery ● Fill in skeletal or incomplete specifications about a domain ○ Large, complex AI systems cannot be completely derived by hand and require dynamic updating to incorporate new information. ○ Learning new characteristics expands the domain or expertise and lessens the “brittleness” of the system ● Build software agents that can adapt to their users or to other software agents ● Reproduce an important aspect of intelligent behavior Why Learn? 9
@giribio Girinath G. Pillai, PhD @giribio Specifying the task T, the performance P and the experience E defines the learning problem. Specifying the learning system requires us to define: – Exactly what knowledge is to be learnt – How this knowledge is to be represented – How this knowledge is to be learnt Specify Learning System 10
@giribio Girinath G. Pillai, PhD @giribio 11
@giribio Girinath G. Pillai, PhD @giribio 12
@giribio Girinath G. Pillai, PhD @giribio 13 History of AI
@giribio Girinath G. Pillai, PhD @giribio 14
@giribio Girinath G. Pillai, PhD @giribio 15
@giribio Girinath G. Pillai, PhD @giribio Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data. Machine Learning 16
@giribio Girinath G. Pillai, PhD @giribio ● Many machine learning systems can be viewed as an iterative process of ○ produce a result, ○ evaluate it against the expected results ○ tweak the system ● Machine learning is also used for systems which discover patterns without prior expected results. ● May be open or black box ○ Open: changes are clearly visible in KB and understandable to humans ○ Black Box: changes are to a system whose internals are not readily visible or understandable. Learning Systems 17
@giribio Girinath G. Pillai, PhD @giribio ● Any learning system needs to somehow implement four components: ○ Knowledge base: what is being learned. Representation of a problem space or domain. ○ Performer: does something with the knowledge base to produce results ○ Critic: evaluates results produced against expected results ○ Learner: takes output from critic and modifies something in KB or performer. ● May also need a “problem generator” to test performance against. Learner Architecture 18
@giribio Girinath G. Pillai, PhD @giribio ● Rote learning ○ Hand-encoded mapping from inputs to stored representation. “Learning by memorization.” ● Interactive learning ○ Human/system interaction producing explicit mapping. ● Induction ○ Using specific examples to reach general conclusions. ● Analogy ○ Determining correspondence between two different representations. Case-based reasoning ● Clustering ○ Unsupervised identification of natural groups in data ● Discovery ○ Unsupervised, specific goal not given ● Genetic algorithms ○ “Evolutionary” search techniques, based on an analogy to “survival of the fittest” Major Paradigms of ML 19
@giribio Girinath G. Pillai, PhD @giribio 20
@giribio Girinath G. Pillai, PhD @giribio a) Supervised Learning b) Unsupervised Learning c) Semi-supervised Learning d) Reinforcement Learning e) Transduction f) Learning to Learn Types of Techniques in ML 21 a) Decision Trees b) Neural Networks (back propagation) c) Probabilistic networks d) Nearest Neighbor e) Support vector machines 5 Popular Algorithms in ML
@giribio Girinath G. Pillai, PhD @giribio a) Model building b) Model testing c) Applying the model Stages of ML 22 a) Artificial Intelligence b) Rule based inference What is not ML?
@giribio Girinath G. Pillai, PhD @giribio 02 ML in Chemistry Chemical data in DS 23
@giribio Girinath G. Pillai, PhD @giribio ML is overhyped or not? 24 C&EN, Sam Lemonick 2018 Vol 96, Issue 34
@giribio Girinath G. Pillai, PhD @giribio ML Workflow in Chemistry 25 Rodrigues Jr et al. A survey on Big Data and Machine Learning for Chemistry
@giribio Girinath G. Pillai, PhD @giribio Chemistry Data used in ML 26https://chemintelligence.com/ Project-oriented datasets Fundamentals of working with active learning algorithms Framework for working with a in-house database
@giribio Girinath G. Pillai, PhD @giribio Chemistry Data used in ML 27 Public databases Using NN to predict reaction conditionsData from simulations https://chemintelligence.com/
@giribio Girinath G. Pillai, PhD @giribio 28 doi.org/10.3389/fchem.2019.00809 DL algorithms for solving different chemical challenges and the respective tasks
@giribio Girinath G. Pillai, PhD @giribio 29 doi.org/10.3389/fchem.2019.00809 Schematic representation of the main components of atomistic ML
@giribio Girinath G. Pillai, PhD @giribio 30 Meta-analysis of DNN-based model performance relative to state-of-the-art non-DNN models in various computational chemistry applications Deep Learning for Computational Chemistry. Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
@giribio Girinath G. Pillai, PhD @giribio Descriptors for Chemistry 31 Issues for ML: ● arbitrary size ● arbitrary order Ideal features: ● general ● compact ● unique ● invariant * ● smooth ● fast 010110101010001011100100010001111110 ML methods need a computer-friendly way to input the atomistic system: easy for us easy for CPU * invariants are determined by the physics of the quantity to predict from the descriptor!
@giribio Girinath G. Pillai, PhD @giribio Descriptors for Chemistry 32 010110101010001011100100010001111110 ML methods need a computer-friendly way to input the atomistic system: Global Descriptor 110100011110000110010111111110 110100011110001011100001111110 010110101010001011100001111110 Local/Atomic Descriptor
@giribio Girinath G. Pillai, PhD @giribio 03 Drug Discovery How to avoid failures? 33
@giribio Girinath G. Pillai, PhD @giribio 34
@giribio Girinath G. Pillai, PhD @giribio Drug Discovery Approaches 35
@giribio Girinath G. Pillai, PhD @giribio Mol. Docking - then and now! 36 1894 The Key-Lock Hypothesis “To exercise a chemical action a ligand interacting with a protein must fit into the binding cavity like a key into a key hole”. (Emil Fischer) 2020 This is only half of the truth…
@giribio Girinath G. Pillai, PhD @giribio Why Molecular Docking? Determine the optimal binding structure of a ligand (a drug candidate, a small molecule) to a receptor (a drug target, a protein or DNA) and quantify the strength of the ligand-receptor interaction. ● Where the ligand will bind? ● How will it bind? ● How strong? ● Role of solvation/desolvation? ● What make a ligand binds to the receptor better than the others? ● Translation and rotation of ligands ● Torsions 37
@giribio Girinath G. Pillai, PhD @giribio 38 3D Lead Optimization - Workflow
@giribio Girinath G. Pillai, PhD @giribio Desolvation Terms 39
@giribio Girinath G. Pillai, PhD @giribio Where is docking score? 40 Green = frequently observed in CSD small molecule crystals Yellow = unusual, however several times observed in CSD Red = very rarely observed in CSD Green = good for affinity Red = bad for affinity Larger the size, stronger the contribution.
@giribio Girinath G. Pillai, PhD @giribio 41 Leads – Desirable properties
@giribio Girinath G. Pillai, PhD @giribio Inter & Intra Ligand Clashes 42
@giribio Girinath G. Pillai, PhD @giribio 43 Hoffmann et al, DDT 2019, 25, 4 Chemical Spaces
@giribio Girinath G. Pillai, PhD @giribio 44 How does the search work?
@giribio Girinath G. Pillai, PhD @giribio 45 How mapping works?
@giribio Girinath G. Pillai, PhD @giribio Retro Synthetic Pathway - ICsynth 46
@giribio Girinath G. Pillai, PhD @giribio ➔ Identify chemistries with an ● optimal balance of properties ➔ Quickly identify situations when ● such a balance is not possible ➔ Fail fast, fail cheap ➔ Only when confident ➔ Avoid missed opportunities The Objectives of Drug Discovery Multi-parameter optimisation 47
@giribio Girinath G. Pillai, PhD @giribio Data Overload - Challenge? 48
@giribio Girinath G. Pillai, PhD @giribio Apply data to Guide Decisions 49 In silico In vitro In vivo Importance Uncertainty Quality Diversity ‘Manual’
@giribio Girinath G. Pillai, PhD @giribio Prioritization 50 Potency Absorption Metabolic Stability
@giribio Girinath G. Pillai, PhD @giribio ● A Rule is a set of property criteria that in combination identify ‘good’ compounds, e.g. ● For example, Lipinski’s Rule of Five: What is a Rule? 51
@giribio Girinath G. Pillai, PhD @giribio ● 74% of marketed CNS drugs achieved CNS MPO > 4 vs. 60% of Pfizer candidates ● Correlations observed between high CNS MPO score and good in vitro ADME properties, e.g. MDCK Papp , HLM stability, P-gp transport Avoid Missed Opportunities 52 CNS MPO Score* CNS MPO = sum of desirabilities for each parameter
@giribio Girinath G. Pillai, PhD @giribio ADME? 53
@giribio Girinath G. Pillai, PhD @giribio QSAR 54 Experimental Assay Activity/Property Chemical, Physical, Biomedical x Molecular Descriptors y Response Variable Molecular Structure Statistical/Machine Learning Modelling Validation Prediction y = f (x) f (x) ?? Experimental Data PredictedData Molecular Structure Descriptor Calc. Classification Feature Selection Model Generation Validation
@giribio Girinath G. Pillai, PhD @giribio • Split data set • Calculate descriptors (2D SMARTS, logP, TPSA, MW, charge etc.) • Multiple modelling techniques • Select the best model by performance on the validation set • Test with an independent set Model Generation 55 Data Set train validate test Build models PLS RBF GPs RF Best model Evaluate models Test the Best model
@giribio Girinath G. Pillai, PhD @giribio • The diversity of the training set defines the domain of applicability of the model • The position of a new compound relative to chemical space impacts the confidence in the prediction Domain of Applicability 56 Descriptor 1 Descriptor2
@giribio Girinath G. Pillai, PhD @giribio ➔ Captopril Capoten®, Bristol Myers-Squibb ➔ Dorzolamide Trusopt®, Merck ➔ Zanamivir Relenza®, Gilead Sciences ➔ Aliskiren Tekturna®, Novartis ➔ Boceprevir Schering-Plough ➔ Nolatrexed dihydrochloride Thymitaq®, Agouron ➔ LY-517717 Lilly/Protherics ➔ Acetyl-CoA carboxylase Inhibitor Nimbus, US ➔ Rupintrivir AG7088, Agouron ➔ NVP-AUY922 Novartis ➔ Vemurafenib Plexxikon ➔ Venetoclax AbbVie, Genentech ➔ Erdafitinib Johnson & Johnson ➔ Verubecestat Merck Selected Success Stories of CADD 57 Nat Rev Drug Discov. 2016 Sep;15(9):605-19 Curr Top Med Chem. 2010;10(1):127-41 http://practicalfragments.blogspot.com/
@giribio Girinath G. Pillai, PhD @giribio 58 Selected Extensions/Nodes Some are under Partner, Community categories Some requires additionally installed binaries and some extensions comes with binaries Commercial extensions requires license from providers Selected Drug Discovery KNIME Nodes
@giribio Girinath G. Pillai, PhD @giribio Open Source KNIME Nodes 59
@giribio Girinath G. Pillai, PhD @giribio Selected Commercial Nodes 60
@giribio Girinath G. Pillai, PhD @giribio Env. Toxicity Predictions 61
@giribio Girinath G. Pillai, PhD @giribio Energy = E E is an approximate activation energy for the reaction of the catalytic site of a CYP with the molecule at this atom. in kJ/mol. Accessibility = A The accessibility is a relative measure of the topological distance for an atom from the center of the molecule, and is always a number between 0.5 (atom at the center) and 1 (atom at the end). Solvent Accessible Surface Area = SASA The SASA describes the local accessibility of an atom and is computed using the 2DSASA algorithm which predicts this value from the molecular topology Site of Metabolism - 3A4 , 2D6, and 2C9 62
@giribio Girinath G. Pillai, PhD @giribio 63
@giribio Girinath G. Pillai, PhD @giribio Ageing Calculate Biological Age? 64
@giribio Girinath G. Pillai, PhD @giribio Gerontology Study of the social, cultural, psychological, cognitive, and biological aspects of ageing. Word was coined by Ilya Ilyich Mechnikov in 1903 Geriatrics is a medical specialty focused on care and treatment of older persons. 65
@giribio Girinath G. Pillai, PhD @giribio Types of Age 66
@giribio Girinath G. Pillai, PhD @giribio 04 What to do Next? Are you ready for AI/ML? 67
@giribio Girinath G. Pillai, PhD @giribio Bio/Pharma 4.0 68 Source : dzone.com
@giribio Girinath G. Pillai, PhD @giribio Digital Transforms 69
@giribio Girinath G. Pillai, PhD @giribio Asia Pacific Market IoT 70
@giribio Girinath G. Pillai, PhD @giribio 38yrs for Radio to reach 35mi people 13yrs for TV 9yrs for iPhone 3yrs for Internet 1yr for Facebook 9mo for Twitter 35days for Angry Bird 19 days for Pokemon Where are we/am I? 71 Per second what happens in Internet 3.88mi Google searches 4.3mi Youtube videos 155 emails 1.2mb/per person 65% Students will not have ready Jobs when they Graduate! As per current skill set
@giribio Girinath G. Pillai, PhD @giribio ● Graduates with right attitude and aptitude - CBI/Pearson Survey 2015 ● Communication ● Individuality – Behavioral Traits ● Critical Thinking ● Collaboration ● Etiquette & Manners ● Accountability & Responsibility ● Work Life Balance & Priorities ● Career Building ● SKILL SET Future Tech Ready 72
@giribio Girinath G. Pillai, PhD @giribio 73 Path and Future of AI
@giribio Girinath G. Pillai, PhD @giribio 74
@giribio Girinath G. Pillai, PhD @giribio CONCLUSION Initially consider 25% score/qlty & 75% diversity as the size of the lead reduces consider 75% score/qlty & 25% diversity. Consider Enrichment factors Good synergetics between human expertise & computational tools Avoid Missed Opportunities Understand significance of parameters/properties Evaluate and decide the tool/approach Check reliability of data used 75
@giribio Girinath G. Pillai, PhD @giribio CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik THANKS Do you have any questions? @giribio 76

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Machine Learning in Chemistry and Drug Candidate Selection

  • 1. @giribio Girinath G. Pillai, PhD @giribio Machine Learning in Drug Discovery for Drug Candidate Selection @giribio Girinath G. Pillai, PhD 1
  • 2. @giribio Girinath G. Pillai, PhD @giribio ● We are not yet completely ready with AI/ML in Drug Discovery (it takes time like Human Genome Project) ● Slides contains contents/pictures/videos taken from web, articles, lectures, tutorials and its respective authors own their copyrights. Technical Slides : slideshare.net/giribio Case Studies : youtube.com/giribio Workflows & Notebooks : github.com/giribio NOTE 2
  • 3. @giribio Girinath G. Pillai, PhD @giribio National Employability 3
  • 4. @giribio Girinath G. Pillai, PhD @giribio Bridge the Gap 4
  • 5. @giribio Girinath G. Pillai, PhD @giribio Industry 4.0 5
  • 6. @giribio Girinath G. Pillai, PhD @giribio AGENDA 01 AI & Machine Learning What? Why? How? 03 Drug Discovery How to avoid failures? 02 ML in Chemistry Chemical data in DS 04 What to do Next? Are you ready for AI/ML? 6
  • 7. @giribio Girinath G. Pillai, PhD @giribio 01 AI & Machine Learning What? Why? How? 7
  • 8. @giribio Girinath G. Pillai, PhD @giribio “Learning denotes changes in a system that ... enable a system to do the same task more efficiently the next time” —Herbert Simon 8
  • 9. @giribio Girinath G. Pillai, PhD @giribio ● Understand and improve efficiency of human learning ○ Improve methods for teaching and tutoring people (better CAI) ● Discover new things or structure that were previously unknown to humans ○ Examples: data mining, scientific discovery ● Fill in skeletal or incomplete specifications about a domain ○ Large, complex AI systems cannot be completely derived by hand and require dynamic updating to incorporate new information. ○ Learning new characteristics expands the domain or expertise and lessens the “brittleness” of the system ● Build software agents that can adapt to their users or to other software agents ● Reproduce an important aspect of intelligent behavior Why Learn? 9
  • 10. @giribio Girinath G. Pillai, PhD @giribio Specifying the task T, the performance P and the experience E defines the learning problem. Specifying the learning system requires us to define: – Exactly what knowledge is to be learnt – How this knowledge is to be represented – How this knowledge is to be learnt Specify Learning System 10
  • 11. @giribio Girinath G. Pillai, PhD @giribio 11
  • 12. @giribio Girinath G. Pillai, PhD @giribio 12
  • 13. @giribio Girinath G. Pillai, PhD @giribio 13 History of AI
  • 14. @giribio Girinath G. Pillai, PhD @giribio 14
  • 15. @giribio Girinath G. Pillai, PhD @giribio 15
  • 16. @giribio Girinath G. Pillai, PhD @giribio Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data. Machine Learning 16
  • 17. @giribio Girinath G. Pillai, PhD @giribio ● Many machine learning systems can be viewed as an iterative process of ○ produce a result, ○ evaluate it against the expected results ○ tweak the system ● Machine learning is also used for systems which discover patterns without prior expected results. ● May be open or black box ○ Open: changes are clearly visible in KB and understandable to humans ○ Black Box: changes are to a system whose internals are not readily visible or understandable. Learning Systems 17
  • 18. @giribio Girinath G. Pillai, PhD @giribio ● Any learning system needs to somehow implement four components: ○ Knowledge base: what is being learned. Representation of a problem space or domain. ○ Performer: does something with the knowledge base to produce results ○ Critic: evaluates results produced against expected results ○ Learner: takes output from critic and modifies something in KB or performer. ● May also need a “problem generator” to test performance against. Learner Architecture 18
  • 19. @giribio Girinath G. Pillai, PhD @giribio ● Rote learning ○ Hand-encoded mapping from inputs to stored representation. “Learning by memorization.” ● Interactive learning ○ Human/system interaction producing explicit mapping. ● Induction ○ Using specific examples to reach general conclusions. ● Analogy ○ Determining correspondence between two different representations. Case-based reasoning ● Clustering ○ Unsupervised identification of natural groups in data ● Discovery ○ Unsupervised, specific goal not given ● Genetic algorithms ○ “Evolutionary” search techniques, based on an analogy to “survival of the fittest” Major Paradigms of ML 19
  • 20. @giribio Girinath G. Pillai, PhD @giribio 20
  • 21. @giribio Girinath G. Pillai, PhD @giribio a) Supervised Learning b) Unsupervised Learning c) Semi-supervised Learning d) Reinforcement Learning e) Transduction f) Learning to Learn Types of Techniques in ML 21 a) Decision Trees b) Neural Networks (back propagation) c) Probabilistic networks d) Nearest Neighbor e) Support vector machines 5 Popular Algorithms in ML
  • 22. @giribio Girinath G. Pillai, PhD @giribio a) Model building b) Model testing c) Applying the model Stages of ML 22 a) Artificial Intelligence b) Rule based inference What is not ML?
  • 23. @giribio Girinath G. Pillai, PhD @giribio 02 ML in Chemistry Chemical data in DS 23
  • 24. @giribio Girinath G. Pillai, PhD @giribio ML is overhyped or not? 24 C&EN, Sam Lemonick 2018 Vol 96, Issue 34
  • 25. @giribio Girinath G. Pillai, PhD @giribio ML Workflow in Chemistry 25 Rodrigues Jr et al. A survey on Big Data and Machine Learning for Chemistry
  • 26. @giribio Girinath G. Pillai, PhD @giribio Chemistry Data used in ML 26https://chemintelligence.com/ Project-oriented datasets Fundamentals of working with active learning algorithms Framework for working with a in-house database
  • 27. @giribio Girinath G. Pillai, PhD @giribio Chemistry Data used in ML 27 Public databases Using NN to predict reaction conditionsData from simulations https://chemintelligence.com/
  • 28. @giribio Girinath G. Pillai, PhD @giribio 28 doi.org/10.3389/fchem.2019.00809 DL algorithms for solving different chemical challenges and the respective tasks
  • 29. @giribio Girinath G. Pillai, PhD @giribio 29 doi.org/10.3389/fchem.2019.00809 Schematic representation of the main components of atomistic ML
  • 30. @giribio Girinath G. Pillai, PhD @giribio 30 Meta-analysis of DNN-based model performance relative to state-of-the-art non-DNN models in various computational chemistry applications Deep Learning for Computational Chemistry. Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
  • 31. @giribio Girinath G. Pillai, PhD @giribio Descriptors for Chemistry 31 Issues for ML: ● arbitrary size ● arbitrary order Ideal features: ● general ● compact ● unique ● invariant * ● smooth ● fast 010110101010001011100100010001111110 ML methods need a computer-friendly way to input the atomistic system: easy for us easy for CPU * invariants are determined by the physics of the quantity to predict from the descriptor!
  • 32. @giribio Girinath G. Pillai, PhD @giribio Descriptors for Chemistry 32 010110101010001011100100010001111110 ML methods need a computer-friendly way to input the atomistic system: Global Descriptor 110100011110000110010111111110 110100011110001011100001111110 010110101010001011100001111110 Local/Atomic Descriptor
  • 33. @giribio Girinath G. Pillai, PhD @giribio 03 Drug Discovery How to avoid failures? 33
  • 34. @giribio Girinath G. Pillai, PhD @giribio 34
  • 35. @giribio Girinath G. Pillai, PhD @giribio Drug Discovery Approaches 35
  • 36. @giribio Girinath G. Pillai, PhD @giribio Mol. Docking - then and now! 36 1894 The Key-Lock Hypothesis “To exercise a chemical action a ligand interacting with a protein must fit into the binding cavity like a key into a key hole”. (Emil Fischer) 2020 This is only half of the truth…
  • 37. @giribio Girinath G. Pillai, PhD @giribio Why Molecular Docking? Determine the optimal binding structure of a ligand (a drug candidate, a small molecule) to a receptor (a drug target, a protein or DNA) and quantify the strength of the ligand-receptor interaction. ● Where the ligand will bind? ● How will it bind? ● How strong? ● Role of solvation/desolvation? ● What make a ligand binds to the receptor better than the others? ● Translation and rotation of ligands ● Torsions 37
  • 38. @giribio Girinath G. Pillai, PhD @giribio 38 3D Lead Optimization - Workflow
  • 39. @giribio Girinath G. Pillai, PhD @giribio Desolvation Terms 39
  • 40. @giribio Girinath G. Pillai, PhD @giribio Where is docking score? 40 Green = frequently observed in CSD small molecule crystals Yellow = unusual, however several times observed in CSD Red = very rarely observed in CSD Green = good for affinity Red = bad for affinity Larger the size, stronger the contribution.
  • 41. @giribio Girinath G. Pillai, PhD @giribio 41 Leads – Desirable properties
  • 42. @giribio Girinath G. Pillai, PhD @giribio Inter & Intra Ligand Clashes 42
  • 43. @giribio Girinath G. Pillai, PhD @giribio 43 Hoffmann et al, DDT 2019, 25, 4 Chemical Spaces
  • 44. @giribio Girinath G. Pillai, PhD @giribio 44 How does the search work?
  • 45. @giribio Girinath G. Pillai, PhD @giribio 45 How mapping works?
  • 46. @giribio Girinath G. Pillai, PhD @giribio Retro Synthetic Pathway - ICsynth 46
  • 47. @giribio Girinath G. Pillai, PhD @giribio ➔ Identify chemistries with an ● optimal balance of properties ➔ Quickly identify situations when ● such a balance is not possible ➔ Fail fast, fail cheap ➔ Only when confident ➔ Avoid missed opportunities The Objectives of Drug Discovery Multi-parameter optimisation 47
  • 48. @giribio Girinath G. Pillai, PhD @giribio Data Overload - Challenge? 48
  • 49. @giribio Girinath G. Pillai, PhD @giribio Apply data to Guide Decisions 49 In silico In vitro In vivo Importance Uncertainty Quality Diversity ‘Manual’
  • 50. @giribio Girinath G. Pillai, PhD @giribio Prioritization 50 Potency Absorption Metabolic Stability
  • 51. @giribio Girinath G. Pillai, PhD @giribio ● A Rule is a set of property criteria that in combination identify ‘good’ compounds, e.g. ● For example, Lipinski’s Rule of Five: What is a Rule? 51
  • 52. @giribio Girinath G. Pillai, PhD @giribio ● 74% of marketed CNS drugs achieved CNS MPO > 4 vs. 60% of Pfizer candidates ● Correlations observed between high CNS MPO score and good in vitro ADME properties, e.g. MDCK Papp , HLM stability, P-gp transport Avoid Missed Opportunities 52 CNS MPO Score* CNS MPO = sum of desirabilities for each parameter
  • 53. @giribio Girinath G. Pillai, PhD @giribio ADME? 53
  • 54. @giribio Girinath G. Pillai, PhD @giribio QSAR 54 Experimental Assay Activity/Property Chemical, Physical, Biomedical x Molecular Descriptors y Response Variable Molecular Structure Statistical/Machine Learning Modelling Validation Prediction y = f (x) f (x) ?? Experimental Data PredictedData Molecular Structure Descriptor Calc. Classification Feature Selection Model Generation Validation
  • 55. @giribio Girinath G. Pillai, PhD @giribio • Split data set • Calculate descriptors (2D SMARTS, logP, TPSA, MW, charge etc.) • Multiple modelling techniques • Select the best model by performance on the validation set • Test with an independent set Model Generation 55 Data Set train validate test Build models PLS RBF GPs RF Best model Evaluate models Test the Best model
  • 56. @giribio Girinath G. Pillai, PhD @giribio • The diversity of the training set defines the domain of applicability of the model • The position of a new compound relative to chemical space impacts the confidence in the prediction Domain of Applicability 56 Descriptor 1 Descriptor2
  • 57. @giribio Girinath G. Pillai, PhD @giribio ➔ Captopril Capoten®, Bristol Myers-Squibb ➔ Dorzolamide Trusopt®, Merck ➔ Zanamivir Relenza®, Gilead Sciences ➔ Aliskiren Tekturna®, Novartis ➔ Boceprevir Schering-Plough ➔ Nolatrexed dihydrochloride Thymitaq®, Agouron ➔ LY-517717 Lilly/Protherics ➔ Acetyl-CoA carboxylase Inhibitor Nimbus, US ➔ Rupintrivir AG7088, Agouron ➔ NVP-AUY922 Novartis ➔ Vemurafenib Plexxikon ➔ Venetoclax AbbVie, Genentech ➔ Erdafitinib Johnson & Johnson ➔ Verubecestat Merck Selected Success Stories of CADD 57 Nat Rev Drug Discov. 2016 Sep;15(9):605-19 Curr Top Med Chem. 2010;10(1):127-41 http://practicalfragments.blogspot.com/
  • 58. @giribio Girinath G. Pillai, PhD @giribio 58 Selected Extensions/Nodes Some are under Partner, Community categories Some requires additionally installed binaries and some extensions comes with binaries Commercial extensions requires license from providers Selected Drug Discovery KNIME Nodes
  • 59. @giribio Girinath G. Pillai, PhD @giribio Open Source KNIME Nodes 59
  • 60. @giribio Girinath G. Pillai, PhD @giribio Selected Commercial Nodes 60
  • 61. @giribio Girinath G. Pillai, PhD @giribio Env. Toxicity Predictions 61
  • 62. @giribio Girinath G. Pillai, PhD @giribio Energy = E E is an approximate activation energy for the reaction of the catalytic site of a CYP with the molecule at this atom. in kJ/mol. Accessibility = A The accessibility is a relative measure of the topological distance for an atom from the center of the molecule, and is always a number between 0.5 (atom at the center) and 1 (atom at the end). Solvent Accessible Surface Area = SASA The SASA describes the local accessibility of an atom and is computed using the 2DSASA algorithm which predicts this value from the molecular topology Site of Metabolism - 3A4 , 2D6, and 2C9 62
  • 63. @giribio Girinath G. Pillai, PhD @giribio 63
  • 64. @giribio Girinath G. Pillai, PhD @giribio Ageing Calculate Biological Age? 64
  • 65. @giribio Girinath G. Pillai, PhD @giribio Gerontology Study of the social, cultural, psychological, cognitive, and biological aspects of ageing. Word was coined by Ilya Ilyich Mechnikov in 1903 Geriatrics is a medical specialty focused on care and treatment of older persons. 65
  • 66. @giribio Girinath G. Pillai, PhD @giribio Types of Age 66
  • 67. @giribio Girinath G. Pillai, PhD @giribio 04 What to do Next? Are you ready for AI/ML? 67
  • 68. @giribio Girinath G. Pillai, PhD @giribio Bio/Pharma 4.0 68 Source : dzone.com
  • 69. @giribio Girinath G. Pillai, PhD @giribio Digital Transforms 69
  • 70. @giribio Girinath G. Pillai, PhD @giribio Asia Pacific Market IoT 70
  • 71. @giribio Girinath G. Pillai, PhD @giribio 38yrs for Radio to reach 35mi people 13yrs for TV 9yrs for iPhone 3yrs for Internet 1yr for Facebook 9mo for Twitter 35days for Angry Bird 19 days for Pokemon Where are we/am I? 71 Per second what happens in Internet 3.88mi Google searches 4.3mi Youtube videos 155 emails 1.2mb/per person 65% Students will not have ready Jobs when they Graduate! As per current skill set
  • 72. @giribio Girinath G. Pillai, PhD @giribio ● Graduates with right attitude and aptitude - CBI/Pearson Survey 2015 ● Communication ● Individuality – Behavioral Traits ● Critical Thinking ● Collaboration ● Etiquette & Manners ● Accountability & Responsibility ● Work Life Balance & Priorities ● Career Building ● SKILL SET Future Tech Ready 72
  • 73. @giribio Girinath G. Pillai, PhD @giribio 73 Path and Future of AI
  • 74. @giribio Girinath G. Pillai, PhD @giribio 74
  • 75. @giribio Girinath G. Pillai, PhD @giribio CONCLUSION Initially consider 25% score/qlty & 75% diversity as the size of the lead reduces consider 75% score/qlty & 25% diversity. Consider Enrichment factors Good synergetics between human expertise & computational tools Avoid Missed Opportunities Understand significance of parameters/properties Evaluate and decide the tool/approach Check reliability of data used 75
  • 76. @giribio Girinath G. Pillai, PhD @giribio CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik THANKS Do you have any questions? @giribio 76