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Retchin M, Wang Y, Takaba K, Chodera JD. DrugGym: A testbed for the economics of autonomous drug discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596296. [PMID: 38854082 PMCID: PMC11160604 DOI: 10.1101/2024.05.28.596296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym , a frame-work for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org .
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2
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Dalton DM, Walroth RC, Rouget-Virbel C, Mack KA, Toste FD. Utopia Point Bayesian Optimization Finds Condition-Dependent Selectivity for N-Methyl Pyrazole Condensation. J Am Chem Soc 2024. [PMID: 38804885 DOI: 10.1021/jacs.4c01616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Utopia Point Bayesian Optimization (UPBO) was used to identify reaction conditions that are highly selective for the formation of N1 and N2-methyl-3-aryl pyrazole constitutional isomers. UPBO was used to explore a wide chemical space and identify basic reaction conditions for a typically acid-catalyzed Knorr pyrazole condensation. These studies revealed that selectivity in the reaction stems from a condition-dependent equilibrium of intermediates prior to dehydration. For the N2-methyl isomer reaction pathway, a hemiaminal intermediate was found to form reversibly under the reaction conditions, enabling a highly selective synthesis of the N2 isomer upon dehydrative workup. UPBO was able to successfully optimize conversion and selectivity simultaneously with search spaces of >1 million potential variable combinations without the need for high-performance computational resources.
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Affiliation(s)
- Derek M Dalton
- Department of Synthetic Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Richard C Walroth
- Department of Synthetic Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Caroline Rouget-Virbel
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Kyle A Mack
- Department of Synthetic Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - F Dean Toste
- Department of Chemistry, University of California, Berkeley, California 94720, United States
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3
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Zheng EJ, Valeri JA, Andrews IW, Krishnan A, Bandyopadhyay P, Anahtar MN, Herneisen A, Schulte F, Linnehan B, Wong F, Stokes JM, Renner LD, Lourido S, Collins JJ. Discovery of antibiotics that selectively kill metabolically dormant bacteria. Cell Chem Biol 2024; 31:712-728.e9. [PMID: 38029756 PMCID: PMC11031330 DOI: 10.1016/j.chembiol.2023.10.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/13/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
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Affiliation(s)
- Erica J Zheng
- Program in Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Jacqueline A Valeri
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Ian W Andrews
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Aarti Krishnan
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Parijat Bandyopadhyay
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Melis N Anahtar
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Alice Herneisen
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, MIT, Cambridge, MA 02139, USA
| | - Fabian Schulte
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Brooke Linnehan
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Felix Wong
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario L8N 3Z5, Canada
| | - Lars D Renner
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, 01062 Dresden, Germany
| | - Sebastian Lourido
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, MIT, Cambridge, MA 02139, USA
| | - James J Collins
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA.
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4
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Mauri A, Bertola M. AlvaBuilder: A Software for De Novo Molecular Design. J Chem Inf Model 2024; 64:2136-2142. [PMID: 37399048 PMCID: PMC11005826 DOI: 10.1021/acs.jcim.3c00610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Indexed: 07/04/2023]
Abstract
AlvaBuilder is a software tool for de novo molecular design and can be used to generate novel molecules having desirable characteristics. Such characteristics can be defined using a simple step by step graphical interface, and they can be based on molecular descriptors, on predictions of QSAR/QSPR models, and on matching molecular fragments or used to design compounds similar to a given one. The molecules generated are always syntactically valid since they are composed by combining fragments of molecules taken from a training data set chosen by the user. In this paper, we demonstrate how the software can be used to design new compounds for a defined case study. AlvaBuilder is available at https://www.alvascience.com/alvabuilder/.
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Affiliation(s)
- Andrea Mauri
- Alvascience
Srl, Via Giuseppe Parini,
35, 23900 Lecco, Italy
| | - Matteo Bertola
- Alvascience
Srl, Via Giuseppe Parini,
35, 23900 Lecco, Italy
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5
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Loeffler HH, He J, Tibo A, Janet JP, Voronov A, Mervin LH, Engkvist O. Reinvent 4: Modern AI-driven generative molecule design. J Cheminform 2024; 16:20. [PMID: 38383444 PMCID: PMC10882833 DOI: 10.1186/s13321-024-00812-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open-source reference implementation for generative molecular design where the software is also being used in production to support in-house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.
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Affiliation(s)
- Hannes H Loeffler
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Jiazhen He
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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6
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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7
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Kalikadien AV, Mirza A, Hossaini AN, Sreenithya A, Pidko EA. Paving the road towards automated homogeneous catalyst design. Chempluschem 2024:e202300702. [PMID: 38279609 DOI: 10.1002/cplu.202300702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/20/2023] [Indexed: 01/28/2024]
Abstract
In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.
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Affiliation(s)
- Adarsh V Kalikadien
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Adrian Mirza
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Aydin Najl Hossaini
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Avadakkam Sreenithya
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
| | - Evgeny A Pidko
- Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands
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8
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Koscher BA, Canty RB, McDonald MA, Greenman KP, McGill CJ, Bilodeau CL, Jin W, Wu H, Vermeire FH, Jin B, Hart T, Kulesza T, Li SC, Jaakkola TS, Barzilay R, Gómez-Bombarelli R, Green WH, Jensen KF. Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science 2023; 382:eadi1407. [PMID: 38127734 DOI: 10.1126/science.adi1407] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023]
Abstract
A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.
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Affiliation(s)
- Brent A Koscher
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Richard B Canty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew A McDonald
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin P Greenman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Charles J McGill
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Camille L Bilodeau
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wengong Jin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haoyang Wu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Florence H Vermeire
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brooke Jin
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Travis Hart
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Timothy Kulesza
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shih-Cheng Li
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tommi S Jaakkola
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Klavs F Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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9
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Ivanenkov Y, Zagribelnyy B, Malyshev A, Evteev S, Terentiev V, Kamya P, Bezrukov D, Aliper A, Ren F, Zhavoronkov A. The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry. ACS Med Chem Lett 2023; 14:901-915. [PMID: 37465301 PMCID: PMC10351082 DOI: 10.1021/acsmedchemlett.3c00041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023] Open
Abstract
This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.
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Affiliation(s)
- Yan Ivanenkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Bogdan Zagribelnyy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Alex Malyshev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Sergei Evteev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Victor Terentiev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Petrina Kamya
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, Canada H3B 4W8
| | - Dmitry Bezrukov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Alex Aliper
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Feng Ren
- Insilico
Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
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10
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Janet JP, Mervin L, Engkvist O. Artificial intelligence in molecular de novo design: Integration with experiment. Curr Opin Struct Biol 2023; 80:102575. [PMID: 36966692 DOI: 10.1016/j.sbi.2023.102575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 06/04/2023]
Abstract
In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.
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Affiliation(s)
- Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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11
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Thompson DC, Copeland SM. Serendipity in research and development: The promise of putting into place patterns for paying attention. Drug Discov Today 2023:103648. [PMID: 37263388 DOI: 10.1016/j.drudis.2023.103648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/17/2023] [Accepted: 05/25/2023] [Indexed: 06/03/2023]
Abstract
The concept of serendipity or accidental discovery is typically discussed in the context of organizational research and development (RnD) through narratives involving 'renegade iconoclasts' laboring at the periphery. Recently, robust academic literature has emerged that grounds serendipity epistemologically. In the current work, this literature is introduced in the context of the typical activities of contemporary life science-focused RnD organization. Practical patterns are described that can increase the likelihood of realizing accidental (serendipitous) RnD discoveries. Teaser: We introduce the concept of serendipity in the context of research and development and describe organizational patterns to be adopted to enrich research processes for the likelihood of realizing serendipitous outcomes.
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Affiliation(s)
| | - Samantha M Copeland
- Technology, Policy, and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands
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12
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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 117] [Impact Index Per Article: 117.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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13
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Yoo J, Kim TY, Joung I, Song SO. Industrializing AI/ML during the end-to-end drug discovery process. Curr Opin Struct Biol 2023; 79:102528. [PMID: 36736243 DOI: 10.1016/j.sbi.2023.102528] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 02/04/2023]
Abstract
Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.
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Affiliation(s)
- Jiho Yoo
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118
| | - Tae Yong Kim
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118
| | - InSuk Joung
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118
| | - Sang Ok Song
- Standigm Inc., 3F, 70 Nonhyeon-ro 85-gil, Gangnam-gu, Seoul, South Korea, 06234 +82.2.501.8118.
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14
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Ackloo S, Antolin AA, Bartolome JM, Beck H, Bullock A, Betz UAK, Böttcher J, Brown PJ, Chaturvedi M, Crisp A, Daniels D, Dreher J, Edfeldt K, Edwards AM, Egner U, Elkins J, Fischer C, Glendorf T, Goldberg S, Hartung IV, Hillisch A, Homan E, Knapp S, Köster M, Krämer O, Llaveria J, Lessel U, Lindemann S, Linderoth L, Matsui H, Michel M, Montel F, Mueller-Fahrnow A, Müller S, Owen DR, Saikatendu KS, Santhakumar V, Sanderson W, Scholten C, Schapira M, Sharma S, Shireman B, Sundström M, Todd MH, Tredup C, Venable J, Willson TM, Arrowsmith CH. Target 2035 – an update on private sector contributions. RSC Med Chem 2023. [DOI: 10.1039/d2md00441k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging ‘open’ principles to develop a pharmacological tool for every human protein.
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Affiliation(s)
- Suzanne Ackloo
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - Albert A. Antolin
- ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Catalonia, Spain
| | | | - Hartmut Beck
- Research and Development, Bayer AG, Pharmaceuticals, 42103 Wuppertal, Germany
| | - Alex Bullock
- Center for Medicines Discovery, Old Road Campus, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7DQ, UK
| | | | - Jark Böttcher
- Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria
| | - Peter J. Brown
- Structural Genomics Consortium, University of North Carolina at Chapel Hill, USA
| | - Menorca Chaturvedi
- Boehringer Ingelheim International, Binger Str. 173, D-55216 Ingelheim, Germany
| | - Alisa Crisp
- Division of Cancer Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Danette Daniels
- Foghorn Therapeutics, 500 Technology Square, Suite 700, Cambridge, MA 02139, USA
| | - Jan Dreher
- Research and Development, Bayer AG, Pharmaceuticals, 42103 Wuppertal, Germany
| | - Kristina Edfeldt
- Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Aled M. Edwards
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - Ursula Egner
- Nuvisan Innovation Campus Berlin GmbH, Müllerstraße 178, 13353, Berlin, Germany
| | - Jon Elkins
- Center for Medicines Discovery, Old Road Campus, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7DQ, UK
| | - Christian Fischer
- Discovery Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, USA
| | - Tine Glendorf
- Research & Early Development, Novo Nordisk A/S, Måløv, Denmark
| | - Steven Goldberg
- Janssen Research and Development LLC, San Diego, California, USA
| | - Ingo V. Hartung
- Medicinal Chemistry, Global R&D, Merck Healthcare KGaA, Frankfurter Straße 250, 64293, Darmstadt, Germany
| | - Alexander Hillisch
- Research and Development, Bayer AG, Pharmaceuticals, 42103 Wuppertal, Germany
| | - Evert Homan
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Stefan Knapp
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Frankfurt 60438, Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt, Frankfurt 60438, Germany
| | - Markus Köster
- Boehringer Ingelheim International, Binger Str. 173, D-55216 Ingelheim, Germany
| | - Oliver Krämer
- Boehringer Ingelheim International, Binger Str. 173, D-55216 Ingelheim, Germany
| | - Josep Llaveria
- A Division of Janssen-Cilag S.A., Janssen Research and Development, Toledo, Spain
| | - Uta Lessel
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, D-88397 Biberach an der Riss, Germany
| | | | - Lars Linderoth
- Research & Early Development, Novo Nordisk A/S, Måløv, Denmark
| | - Hisanori Matsui
- Neuroscience Drug Discovery Unit, Research, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Maurice Michel
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Florian Montel
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, D-88397 Biberach an der Riss, Germany
| | | | - Susanne Müller
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Frankfurt 60438, Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt, Frankfurt 60438, Germany
| | - Dafydd R. Owen
- Discovery Network Group, Pfizer Medicine Design, Cambridge, MA 02139, USA
| | - Kumar Singh Saikatendu
- Global Research Externalization, Takeda California, Inc., 9625 Towne Center Drive, San Diego, CA 92121, USA
| | | | - Wendy Sanderson
- Janssen Research & Development, Janssen Pharmaceutica N. V, Beerse, Belgium
| | - Cora Scholten
- Research and Development, Bayer AG, Pharmaceuticals, 13353 Berlin, Germany
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Sujata Sharma
- Janssen Research and Development LLC, San Diego, California, USA
| | - Brock Shireman
- Janssen Research and Development LLC, San Diego, California, USA
| | - Michael Sundström
- Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Matthew H. Todd
- School of Pharmacy, University College London, London, WC1N 1AX, UK
| | - Claudia Tredup
- Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Frankfurt 60438, Germany
- Structural Genomics Consortium, BMLS, Goethe University Frankfurt, Frankfurt 60438, Germany
| | - Jennifer Venable
- Janssen Research and Development LLC, San Diego, California, USA
| | - Timothy M. Willson
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Cheryl H. Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, M5G 1L7, Canada
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15
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Hurben AK, Erber L. Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment. Chem Res Toxicol 2022; 35:1925-1928. [PMID: 36179327 PMCID: PMC9886504 DOI: 10.1021/acs.chemrestox.2c00269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied across various facets of drug design, development, and safety assessment.
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Affiliation(s)
- Alexander K Hurben
- Department of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Luke Erber
- Department of Medicinal Chemistry and Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
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16
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Krenn M, Pollice R, Guo SY, Aldeghi M, Cervera-Lierta A, Friederich P, dos Passos Gomes G, Häse F, Jinich A, Nigam A, Yao Z, Aspuru-Guzik A. On scientific understanding with artificial intelligence. NATURE REVIEWS. PHYSICS 2022; 4:761-769. [PMID: 36247217 PMCID: PMC9552145 DOI: 10.1038/s42254-022-00518-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 05/27/2023]
Abstract
An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Si Yue Guo
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Alba Cervera-Lierta
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Pascal Friederich
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Gabriel dos Passos Gomes
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Florian Häse
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medical College, New York, USA
| | - AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Zhenpeng Yao
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, Ontario Canada
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