Constrained Bayesian optimization for automatic chemical design using variational autoencoders

Automatic Chemical Design is a framework for generating novel molecules with optimized properties.

[1]  Xavier Bresson,et al.  A Two-Step Graph Convolutional Decoder for Molecule Generation , 2019, ArXiv.

[2]  H. Kulik,et al.  A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery , 2019 .

[3]  Hiroshi Kajino,et al.  Molecular Hypergraph Grammar with its Application to Molecular Optimization , 2018, ICML.

[4]  Daniel Hernández-Lobato,et al.  Black-Box Alpha Divergence Minimization , 2015, ICML.

[5]  Kyunghyun Cho,et al.  Conditional molecular design with deep generative models , 2018, J. Chem. Inf. Model..

[6]  Ryan P. Adams,et al.  Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. , 2016, Nature materials.

[7]  Christian Wachinger,et al.  Likelihood-Free Inference and Generation of Molecular Graphs , 2019, ArXiv.

[8]  Seongok Ryu,et al.  Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network , 2018, 1805.10988.

[9]  Isidro Cortes-Ciriano,et al.  Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks , 2018, Journal of chemical information and modeling.

[10]  Donald R. Jones,et al.  Global versus local search in constrained optimization of computer models , 1998 .

[11]  Constantine Bekas,et al.  “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models† †Electronic supplementary information (ESI) available: Time-split test set and example predictions, together with attention weights, confidence and token probabilities. See DO , 2017, Chemical science.

[12]  Dmitry Vetrov,et al.  Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. , 2018, Molecular pharmaceutics.

[13]  Thomas Blaschke,et al.  Application of Generative Autoencoder in De Novo Molecular Design , 2017, Molecular informatics.

[14]  Evgeny Putin,et al.  Adversarial Threshold Neural Computer for Molecular de Novo Design. , 2018, Molecular pharmaceutics.

[15]  Regina Barzilay,et al.  Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.

[16]  Koji Tsuda,et al.  ChemTS: an efficient python library for de novo molecular generation , 2017, Science and technology of advanced materials.

[17]  Edward O. Pyzer-Knapp,et al.  Bayesian optimization for accelerated drug discovery , 2018, IBM J. Res. Dev..

[18]  Jae Yong Ryu,et al.  Deep learning improves prediction of drug–drug and drug–food interactions , 2018, Proceedings of the National Academy of Sciences.

[19]  Niloy Ganguly,et al.  NeVAE: A Deep Generative Model for Molecular Graphs , 2018, AAAI.

[20]  Daniel James,et al.  Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases , 2007, ChemMedChem.

[21]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[22]  Regina Barzilay,et al.  Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network , 2017, NIPS.

[23]  Alán Aspuru-Guzik,et al.  SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry , 2019, ArXiv.

[24]  Alán Aspuru-Guzik,et al.  Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.

[25]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[26]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

[27]  Thomas Bartz-Beielstein,et al.  Model-based methods for continuous and discrete global optimization , 2017, Appl. Soft Comput..

[28]  Jonas Boström,et al.  Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design , 2019, J. Chem. Inf. Model..

[29]  S Mohammadi,et al.  Penalized Variational Autoencoder for Molecular Design , 2019 .

[30]  Peter Ertl,et al.  Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions , 2009, J. Cheminformatics.

[31]  Constantine Bekas,et al.  Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation , 2018, ArXiv.

[32]  D. Ginsbourger,et al.  A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes , 2008 .

[33]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[34]  Nikos Komodakis,et al.  GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders , 2018, ICANN.

[35]  Kirthevasan Kandasamy,et al.  ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations , 2019, AISTATS.

[36]  Qi Liu,et al.  Advances and challenges in deep generative models for de novo molecule generation , 2018, WIREs Computational Molecular Science.

[37]  Alán Aspuru-Guzik,et al.  Reinforced Adversarial Neural Computer for de Novo Molecular Design , 2018, J. Chem. Inf. Model..

[38]  Ola Engkvist,et al.  A de novo molecular generation method using latent vector based generative adversarial network , 2019, J. Cheminformatics.

[39]  Ryan-Rhys Griffiths,et al.  Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation , 2019, Mach. Learn. Sci. Technol..

[40]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[41]  Samy Bengio,et al.  Generating Sentences from a Continuous Space , 2015, CoNLL.

[42]  Regina Barzilay,et al.  Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction , 2017, J. Chem. Inf. Model..

[43]  Thierry Kogej,et al.  Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.

[44]  R. D. Shen.  Automatic Chemical Design with Molecular Graph Variational Autoencoders , 2018 .

[45]  Tristan Aumentado-Armstrong,et al.  Latent Molecular Optimization for Targeted Therapeutic Design , 2018, ArXiv.

[46]  Gianni De Fabritiis,et al.  Shape-Based Generative Modeling for de Novo Drug Design , 2019, J. Chem. Inf. Model..

[47]  Motoki Abe,et al.  GraphNVP: An Invertible Flow Model for Generating Molecular Graphs , 2019, ArXiv.

[48]  Jasper Snoek,et al.  Bayesian Optimization with Unknown Constraints , 2014, UAI.

[49]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[50]  Geun Ho Gu,et al.  Machine learning for renewable energy materials , 2019, Journal of Materials Chemistry A.

[51]  Alán Aspuru-Guzik,et al.  Phoenics: A Bayesian Optimizer for Chemistry , 2018, ACS central science.

[52]  Nicola De Cao,et al.  MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.

[53]  Yibo Li,et al.  Multi-objective de novo drug design with conditional graph generative model , 2018, Journal of Cheminformatics.

[54]  Frank D. Wood,et al.  Bayesian Optimization for Probabilistic Programs , 2017, NIPS.

[55]  Philippe Schwaller.  Data-driven Chemical Reaction Classification with Attention-Based Neural Networks , 2019 .

[56]  Ryan G. Coleman,et al.  ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..

[57]  Lukas Turcani,et al.  Machine Learning for Organic Cage Property Prediction , 2019 .

[58]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[59]  Alán Aspuru-Guzik,et al.  Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space , 2017, ICML.

[60]  Dragos Horvath,et al.  De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping , 2019, J. Chem. Inf. Model..

[61]  Honglak Lee,et al.  Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.

[62]  Petra Schneider,et al.  Generative Recurrent Networks for De Novo Drug Design , 2017, Molecular informatics.

[63]  Ryan-Rhys Griffiths,et al.  Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design , 2018, ArXiv.

[64]  Matt J. Kusner,et al.  Learning a Generative Model for Validity in Complex Discrete Structures , 2017, ICLR.

[65]  Alán Aspuru-Guzik,et al.  Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.

[66]  Jianying Hu,et al.  Predicting adverse drug reactions through interpretable deep learning framework , 2018, BMC Bioinformatics.

[67]  Regina Barzilay,et al.  Learning Multimodal Graph-to-Graph Translation for Molecular Optimization , 2018, ICLR.

[68]  Esben Jannik Bjerrum,et al.  Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders , 2018, Biomolecules.

[69]  Egor Kraev.  Grammars and reinforcement learning for molecule optimization , 2018, ArXiv.

[70]  Li Li,et al.  Optimization of Molecules via Deep Reinforcement Learning , 2018, Scientific Reports.

[71]  Shahar Harel,et al.  Prototype-Based Compound Discovery Using Deep Generative Models. , 2018, Molecular pharmaceutics.

[72]  Alán Aspuru-Guzik,et al.  Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models , 2017, ArXiv.

[73]  Alán Aspuru-Guzik,et al.  The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid , 2011 .

[74]  Alán Aspuru-Guzik,et al.  Accelerating the discovery of materials for clean energy in the era of smart automation , 2018, Nature Reviews Materials.

[75]  Brian Hudson,et al.  Strategic Pooling of Compounds for High-Throughput Screening , 1999, J. Chem. Inf. Comput. Sci..

[76]  Koji Tsuda,et al.  Population-based de novo molecule generation, using grammatical evolution , 2018, 1804.02134.

[77]  Alán Aspuru-Guzik,et al.  Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models , 2018, Frontiers in Pharmacology.

[78]  Michael A. Gelbart,et al.  Constrained Bayesian Optimization and Applications , 2015 .

[79]  Alpha A. Lee,et al.  Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning , 2019, Chemical science.

[80]  Vijay S. Pande,et al.  Massively Multitask Networks for Drug Discovery , 2015, ArXiv.

[81]  Jure Leskovec,et al.  Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.

[82]  Alán Aspuru-Guzik,et al.  Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project , 2014 .

[83]  Jin Woo Kim,et al.  Molecular generative model based on conditional variational autoencoder for de novo molecular design , 2018, Journal of Cheminformatics.

[84]  Marwin H. S. Segler,et al.  GuacaMol: Benchmarking Models for De Novo Molecular Design , 2018, J. Chem. Inf. Model..

[85]  Alán Aspuru-Guzik,et al.  What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery , 2015 .

[86]  James F Blake,et al.  Identification and evaluation of molecular properties related to preclinical optimization and clinical fate. , 2005, Medicinal chemistry (Shariqah (United Arab Emirates)).

[87]  Connor W. Coley,et al.  A graph-convolutional neural network model for the prediction of chemical reactivity , 2018, Chemical science.

[88]  Daniel T. Chang.  Probabilistic Generative Deep Learning for Molecular Design , 2019, ArXiv.

[89]  Daniel C. Elton,et al.  Deep learning for molecular generation and optimization - a review of the state of the art , 2019, Molecular Systems Design & Engineering.

[90]  Andrew C. Good,et al.  An Empirical Process for the Design of High-Throughput Screening Deck Filters , 2006, J. Chem. Inf. Model..

[91]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[92]  G. V. Paolini,et al.  Quantifying the chemical beauty of drugs. , 2012, Nature chemistry.

[93]  Ankur Taly,et al.  Using attribution to decode binding mechanism in neural network models for chemistry , 2018, Proceedings of the National Academy of Sciences.

[94]  Peter I. Frazier,et al.  Bayesian Optimization of Composite Functions , 2019, ICML.

[95]  Benoit Playe.  Machine learning approaches for drug virtual screening. (Méthodes d'apprentissage statistique pour le criblage virtuel de médicament) , 2019 .

[96]  Thomas Blaschke,et al.  Molecular de-novo design through deep reinforcement learning , 2017, Journal of Cheminformatics.

[97]  Eric J. Martin,et al.  In silico generation of novel, drug-like chemical matter using the LSTM neural network , 2017, ArXiv.