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Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning [article]

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
2020 arXiv   pre-print
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  ...  by embedding the concept of synthetic accessibility directly into the de novo drug design system.  ...  Acknowledgements The authors would like to thank Mohammad Amini for thoroughly reviewing the manuscript and Harry Zhao, Sitao Luan and Scott Fujimoto for useful discussions and feedback. 99andBeyond would  ... 
arXiv:2004.12485v2 fatcat:w2ews74xt5cwjm6mgfm3cucn4i

Artificial intelligence in chemistry and drug design

Nathan Brown, Peter Ertl, Richard Lewis, Torsten Luksch, Daniel Reker, Nadine Schneider
2020 Journal of Computer-Aided Molecular Design  
Acknowledgements We would like to specially thank all the authors of this special issue for their great contributions and all the reviewers for their valuable and critical feedback to ensure high-quality  ...  How to navigate through it efficiently and select molecules that satisfy the multiple parameters that need to be optimized and that are synthetically accessible [5] ?  ...  Big data and navigation in chemical space Analysis of very big chemical datasets is a major research area that can profit from the application of modern machine learning and AI-based methods.  ... 
doi:10.1007/s10822-020-00317-x pmid:32468207 fatcat:ryu54ek3xrfedbjyr3lplumflq

Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering [article]

E. William Webb, Peter J.H. Scott
2021 arXiv   pre-print
Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic.  ...  In this perspective we offer insights into how the technology could be applied to improve the design and synthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling  ...  Disclosure The authors declare that they have no conflicts of interest relating to the subject matter of the present review.  ... 
arXiv:2108.02814v1 fatcat:2v4cqumeungk7nyc6t2vrpojqi

Reinforcement Learning with Real-time Docking of 3D Structures to Cover Chemical Space: Mining for Potent SARS-CoV-2 Main Protease Inhibitors [article]

Jie Li, Oufan Zhang, Fiona L. Kearns, Mojtaba Haghighatlari, Conor Parks, Xingyi Guan, Itai Leven, Rommie E. Amaro, Teresa Head-Gordon
2021 arXiv   pre-print
Ultimately 54 molecules are proposed as potent Mpro inhibitors (7 of which have better synthetic accessibility), covering a much broader range than crowd-sourced projects like the COVID moonshot, and our  ...  We propose a novel framework that generates new inhibitor molecules for target proteins by combining deep reinforcement learning (RL) with real-time molecular docking on 3-dimensional structures.  ...  Learning to navigate the synthetically accessible chemical space using reinforcement learning. International Conference on Machine Learning. 2020; pp 3668-3679. (26) Zhavoronkov, A.; Ivanenkov, Y.  ... 
arXiv:2110.01806v1 fatcat:ck46g2youvb4ljeykybgojok2i

Introducing Machine Learning: Science and Technology

O Anatole von Lilienfeld
2020 Machine Learning: Science and Technology  
and chemical systems, to particle physics, medical imaging, space science, climate science and drug discovery.  ...  Conceived in close consultation with the community, Machine Learning: Science and Technology has been launched as a unique multidisciplinary, open access journal that will bridge the application of machine  ...  In these cases, using numerical solvers and high-performance computing, extensive synthetic data sets can be generated, rendering also these problems amenable to machine learning.  ... 
doi:10.1088/2632-2153/ab6d5d fatcat:okz7ocd4x5e63oa7abmyb4voam

Efficient Multi-Objective Molecular Optimization in a Continuous Latent Space

Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noe, Djork-Arné Clevert
2019 Chemical Science  
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable properties. In this work, we propose a novel method that combines in silico...  ...  FN acknowledges funding from the European Commission (ERC CoG 772230 "ScaleCell") and MATH+ (AA1-6). DC and FM acknowledge funding from the Bayer AG Life Science Collaboration ("DeepMinDS").  ...  Another strategy for ne-tuning a generative model is Reinforcement Learning. 13 Reinforcement Learning aims at learning the optimal set of actions to optimize a dened reward in a given environment.  ... 
doi:10.1039/c9sc01928f pmid:31853357 pmcid:PMC6836962 fatcat:4qhe72drrvag3jow3ov5r646ki

Autonomous discovery in the chemical sciences part I: Progress

Klavs F. Jensen, Connor W Coley, Natalie S Eyke
2019 Angewandte Chemie International Edition  
This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences.  ...  These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling.  ...  This work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium and the DARPA Make-It program under contract ARO W911NF-16-2-0023.  ... 
doi:10.1002/anie.201909987 pmid:31553511 fatcat:yfg4jnixhvfgdmnr4p6mbyolpe

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

Thomas J. Struble, Juan C Alvarez, Scott Brown, Milan Chytil, Justin Cisar, Renee DesJarlais, Ola Engkvist, Scott A. Frank, Daniel R Greve, Daniel J Griffin, Xinjun Hou, Jeffrey W. Johannes (+15 others)
2020 Journal of Medicinal Chemistry  
in silico synthetic planning into their overall approach to accessing target molecules.  ...  A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical  ...  to reach a new chemical space  ... 
doi:10.1021/acs.jmedchem.9b02120 pmid:32243158 pmcid:PMC7457232 fatcat:axwtpbnpy5gidiegi3i7t4b6va

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Alex Zhavoronkov, Quentin Vanhaelen, Tudor I. Oprea
2020 Clinical Pharmacology and Therapeutics  
As the field of artificial intelligence and machine learning (AI/ML) for drug discovery is rapidly advancing, we address the question "what is the impact of recent AI/ML trends in the area of Clinical  ...  We briefly discuss current trends in the use of AI/ML in healthcare and the impact of AI/ML context of the daily practice of clinical pharmacologists.  ...  This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly  ... 
doi:10.1002/cpt.1795 pmid:31957003 pmcid:PMC7158211 fatcat:dtowooen6fhwvhnnew2e75nify

Inverse design with deep generative models: next step in materials discovery

Shuaihua Lu, Qionghua Zhou, Xinyu Chen, Zhilong Song, Jinlan Wang
2022 National Science Review  
Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure  ...  Many advanced ML algorithms are widely used, including active learning and transfer learning, which enable the exploration of chemical space more efficiently.  ...  Beyond theory, closed-loop approaches for material discovery using generative-model-based inverse design will be capable of navigating and searching chemical space quickly, efficiently and, importantly  ... 
doi:10.1093/nsr/nwac111 pmid:35992238 pmcid:PMC9385454 fatcat:rcq7bpyxqnetjhhwf5kxtappiu

Inverse molecular design using machine learning: Generative models for matter engineering

Benjamin Sanchez-Lengeling, Alán Aspuru-Guzik
2018 Science  
Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular  ...  The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable.  ...  Inverse design (Fig. 2) uses optimization, sampling, and search methods to navigate the manifold of functionality of chemical space (17, 18) .  ... 
doi:10.1126/science.aat2663 pmid:30049875 fatcat:w5jlvvsq4jec3a3t7t4pisr7sq

A Brief Review of Machine Learning-Based Bioactive Compound Research

Jihye Park, Bo Ram Beck, Hoo Hyun Kim, Sangbum Lee, Keunsoo Kang
2022 Applied Sciences  
In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces  ...  This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds.  ...  Previously, navigating the entire chemical space was thought to be impractical due to mostly technical limitations.  ... 
doi:10.3390/app12062906 fatcat:5ootoacaxzeh7etmwk3gwvokci

PaccMann^RL on SARS-CoV-2: Designing antiviral candidates with conditional generative models [article]

Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez
2020 arXiv   pre-print
Exploiting this multi-objective as a reward function of a conditional molecular generator (consisting of two VAEs), we showcase a framework that navigates the chemical space toward regions with more antiviral  ...  We present a case-study on a potential Envelope-protein inhibitor and perform a synthetic accessibility assessment of the best generated molecules is performed that resembles a viable roadmap towards a  ...  But it gives evidence that our model successfully navigates the chemical space towards regions of high reward.  ... 
arXiv:2005.13285v3 fatcat:w7i7zarvdbfalgrymwisa3gspe

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Douglas B Kell, Soumitra Samanta, Neil Swainston
2020 Biochemical Journal  
In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical  ...  The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny.  ...  We apologise to authors whose contributions were not included due to lack of space.  ... 
doi:10.1042/bcj20200781 pmid:33290527 pmcid:PMC7733676 fatcat:ujd4s2xyfrcjtgqef44jf5yihm

Fragment-based molecular generative model with high generalization ability and synthetic accessibility [article]

Seonghwan Seo, Jaechang Lim, Woo Youn Kim
2021 arXiv   pre-print
The former becomes possible by learning the contribution of individual fragments to the target properties in an auto-regressive manner.  ...  Molecular fragments such as functional groups are more closely related to molecular properties and synthetic accessibility than atoms.  ...  Acknowledgements This work was supported by the Tech Incubator Program for Startup (TIPS) funded by the Ministry of SMEs and Startups (MSS, Korea) (S3031674). Notes and references  ... 
arXiv:2111.12907v1 fatcat:nosl47f4pvbizjbytwddhrzjei
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