Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
andBeyond（99andBeyond）；Clemson University（克莱姆森大学）；IIIT Hyderabad（海得拉巴国际信息技术学院）；University of Delaware（特拉华大学）；University of Montreal（蒙特利尔大学）；Mila - Quebec AI Institute（Mila - 魁北克人工智能研究所）；HEC Montréal（蒙特利尔高等商学院）
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, … 展开作者
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio
备注：
added the statistics of top-100 compounds used logP metric with scaled components added values of the initial reactants to the box plots some values in tables are recalculated due to the inconsistent environments on different machines. corresponding benchmarks were rerun with the requirements on github. no significant changes in the results. corrected figures in the Appendix
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models. However, current generative approaches exhibit a significant challenge as they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting commercially available small molecule building blocks to valid chemical reactions at every time step of the iterative virtual multi-step synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. PGFS achieves state-of-the-art performance in generating structures with high QED and penalized clogP. Moreover, we validate PGFS in an in-silico proof-of-concept associated with three HIV targets. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favourably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically-accessible drug-like molecules. This becomes possible by defining transitions in our Markov Decision Proce…
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have optimal target absorption energies in the visible region as well as well-defined ground states. Machine learning (ML) accelerated discovery could overcome such challenges by enabling screening of a larger space, but is limited by the fidelity of the data used in ML model training, which is typically from a single approximate density functional. To addre…
Generating readily synthesizable small molecule fluorophore scaffolds with reinforcement learning
Department of Computer Science, Stanford University（计算机科学系，斯坦福大学）；Department of Molecular and Cellular Physiology, Stanford University（分子与细胞生理学系，斯坦福大学）；Department of Physics, Stanford University（物理系，斯坦福大学）；Department of Biomedical Data Science, Stanford University（生物医学数据科学系，斯坦福大学）；Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus（生物化学与分子遗传学系，科罗拉多大学安舒茨医学校区）；Department of Pharmacology, University of Colorado Anschutz Medical Campus（药理学系，科罗拉多大学安舒茨医学校区）；Department of Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Colorado Anschutz Medical Campus（药学系，斯克格斯药学院，科罗拉多大学安舒茨医学校区）；BioFrontiers Institute, University of Colorado Boulder（生物前沿研究院，科罗拉多大学波德分校）；Greenstone Biosciences（Greenstone 生物科技公司）；Stanford University（斯坦福大学）
The application of reinforcement learning algorithms onto real life problems always bears the challenge of filtering the environmental state out of raw sensor readings. While most approaches use heuristics, biology suggests that there must exist an unsupervised method to construct such filters automatically. Besides the extraction of environmental states, the filters have to represent them in a fashion that support modern reinforcement algorithms. Many popular algorithms use a linear architecture, so one should aim at filters that have good approximation properties in combination with linear f…
Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed gen…
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Stanford University（斯坦福大学）；Arc Institute（Arc研究所）；Gladstone-UCSF Institute of Genomic Immunology（Gladstone-加州大学旧金山分校基因组免疫学研究所）；University of California, San Francisco（加州大学旧金山分校）；UCSF Helen Diller Family Comprehensive Cancer Center（加州大学旧金山分校海伦·迪勒家族综合癌症中心）
Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
Jagiellonian University, Faculty of Mathematics and Computer Science（雅盖隆大学数学与计算机科学学院）；Mila – Québec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；University of Toronto（多伦多大学）；The Hospital for Sick Children Research Institute（儿童医院研究所在）；Acceleration Consortium（加速联盟）；Vector Institute（向量研究所）
EPFL（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Research AI4Science（微软研究院AI4Science）；University of Oxford（牛津大学）
Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically-relevant candidate drug molecules. In this work, we propose DiffLinker, an E(3)-equivariant 3D-conditional diffusion model for molecular linker design. Given a set of disconnected fragments, our model places missing atoms in between and designs a molecule incorporating all the initial fragments. Unlike previous approaches that are only able to connect pairs of molecular fragmen…
