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Saigiridharan L, Hassen AK, Lai H, Torren-Peraire P, Engkvist O, Genheden S. AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application. J Cheminform 2024; 16:57. [PMID: 38778382 PMCID: PMC11112899 DOI: 10.1186/s13321-024-00860-x] [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: 12/14/2023] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
We present an updated overview of the AiZynthFinder package for retrosynthesis planning. Since the first version was released in 2020, we have added a substantial number of new features based on user feedback. Feature enhancements include policies for filter reactions, support for any one-step retrosynthesis model, a scoring framework and several additional search algorithms. To exemplify the typical use-cases of the software and highlight some learnings, we perform a large-scale analysis on several hundred thousand target molecules from diverse sources. This analysis looks at for instance route shape, stock usage and exploitation of reaction space, and points out strengths and weaknesses of our retrosynthesis approach. The software is released as open-source for educational purposes as well as to provide a reference implementation of the core algorithms for synthesis prediction. We hope that releasing the software as open-source will further facilitate innovation in developing novel methods for synthetic route prediction. AiZynthFinder is a fast, robust and extensible open-source software and can be downloaded from https://github.com/MolecularAI/aizynthfinder .
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Affiliation(s)
| | - Alan Kai Hassen
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
| | - Helen Lai
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Paula Torren-Peraire
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Samuel Genheden
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
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2
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Wigh D, Arrowsmith J, Pomberger A, Felton KC, Lapkin AA. ORDerly: Data Sets and Benchmarks for Chemical Reaction Data. J Chem Inf Model 2024; 64:3790-3798. [PMID: 38648077 PMCID: PMC11094788 DOI: 10.1021/acs.jcim.4c00292] [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] [Received: 02/20/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024]
Abstract
Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market. Chemical reactions play a significant role in these fields, but there is a lack of high-quality open-source chemical reaction data sets for training machine learning models. Herein, we present ORDerly, an open-source Python package for the customizable and reproducible preparation of reaction data stored in accordance with the increasingly popular Open Reaction Database (ORD) schema. We use ORDerly to clean United States patent data stored in ORD and generate data sets for forward prediction, retrosynthesis, as well as the first benchmark for reaction condition prediction. We train neural networks on data sets generated with ORDerly for condition prediction and show that data sets missing key cleaning steps can lead to silently overinflated performance metrics. Additionally, we train transformers for forward and retrosynthesis prediction and demonstrate how non-patent data can be used to evaluate model generalization. By providing a customizable open-source solution for cleaning and preparing large chemical reaction data, ORDerly is poised to push forward the boundaries of machine learning applications in chemistry.
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Affiliation(s)
- Daniel
S. Wigh
- Department of Chemical Engineering
and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
| | - Joe Arrowsmith
- Department of Chemical Engineering
and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
| | - Alexander Pomberger
- Department of Chemical Engineering
and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
| | - Kobi C. Felton
- Department of Chemical Engineering
and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
| | - Alexei A. Lapkin
- Department of Chemical Engineering
and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
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3
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Wright BA, Regni A, Chaisan N, Sarpong R. Navigating Excess Complexity: Total Synthesis of Daphenylline. J Am Chem Soc 2024; 146:1813-1818. [PMID: 38207289 DOI: 10.1021/jacs.3c12953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Retrosynthetic analysis is a framework for designing synthetic routes to complex molecules that generally prioritizes disconnections which reduce molecular complexity. However, strict adherence to this principle can overlook pathways involving highly complex intermediates that can be easily prepared through powerful bond-forming transformations. Herein, we demonstrate this tactic of generating excess complexity, followed by strategic bond-cleavage, as a highly effective approach for the 11-step total synthesis of the Daphniphyllum alkaloid daphenylline. To implement this strategy, we accessed a bicyclo[4.1.0]heptane core through a dearomative Buchner cycloaddition, which enabled construction of the seven-membered ring after C-C bond cleavage. Installation of the synthetically challenging quaternary stereocenter methyl group was achieved through a thia-Paternò-Büchi [2 + 2] photocycloaddition followed by stereospecific thietane reduction, further illustrating how building excess complexity can enable desired synthetic outcomes after strategic bond-breaking events. This strategy leveraging bond cleavage transformations should serve as a complement to traditional bond-forming, complexity-generating synthetic strategies.
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Affiliation(s)
- Brandon A Wright
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Alessio Regni
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Nattawadee Chaisan
- Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Richmond Sarpong
- Department of Chemistry, University of California, Berkeley, California 94720, United States
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4
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Takács G, Havasi D, Sándor M, Dohánics Z, Balogh GT, Kiss R. DIY Virtual Chemical Libraries - Novel Starting Points for Drug Discovery. ACS Med Chem Lett 2023; 14:1188-1197. [PMID: 37736187 PMCID: PMC10510501 DOI: 10.1021/acsmedchemlett.3c00146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023] Open
Abstract
The advancement of in silico technologies such as library enumeration and synthetic feasibility prediction has made drug discovery pipelines rely more and more on virtual libraries, which provide a significantly larger pool of compounds than in-stock supplier catalogs. Virtual libraries from external sources, however, may be associated with long delivery time and high cost. In this study, we present a Do-It-Yourself (DIY) combinatorial chemistry library containing over 14 million almost completely novel products built from 1000 low-cost building blocks based on robust reactions frequently applied at medicinal chemistry laboratories. The applicability of the DIY library for various drug discovery approaches is demonstrated by extensive physicochemical property, structural diversity profiling, and the generation of focused libraries. We found that internally built DIY chemical libraries present a viable alternative of external virtual catalogs by providing access to a large number of low-cost and quickly accessible potential chemical starting points for drug discovery.
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Affiliation(s)
- Gergely Takács
- Department
of Chemical and Environmental Process Engineering, Faculty of Chemical
Technology and Biotechnology, Budapest University
of Technology and Economics, Műegyetem rakpart 3, Budapest 1111, Hungary
- Mcule.com
Kft, Bartók Béla
út 105-113, Budapest 1115, Hungary
| | - Dávid Havasi
- Department
of Chemical and Environmental Process Engineering, Faculty of Chemical
Technology and Biotechnology, Budapest University
of Technology and Economics, Műegyetem rakpart 3, Budapest 1111, Hungary
- Mcule.com
Kft, Bartók Béla
út 105-113, Budapest 1115, Hungary
| | - Márk Sándor
- Mcule.com
Kft, Bartók Béla
út 105-113, Budapest 1115, Hungary
| | - Zsolt Dohánics
- Mcule.com
Kft, Bartók Béla
út 105-113, Budapest 1115, Hungary
| | - György T. Balogh
- Department
of Chemical and Environmental Process Engineering, Faculty of Chemical
Technology and Biotechnology, Budapest University
of Technology and Economics, Műegyetem rakpart 3, Budapest 1111, Hungary
- Department
of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Semmelweis University, Hőgyes Endre utca 7-9, Budapest 1092, Hungary
| | - Róbert Kiss
- Mcule.com
Kft, Bartók Béla
út 105-113, Budapest 1115, Hungary
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Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
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Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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Pasquini M, Stenta M. LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes. J Cheminform 2023; 15:41. [PMID: 37005691 PMCID: PMC10067316 DOI: 10.1186/s13321-023-00714-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. RESULTS Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. CONCLUSIONS The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin.
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Affiliation(s)
- Marta Pasquini
- Syngenta Crop Protection AG, Schaffhauserstrasse, 4332, Stein, AG, Switzerland.
| | - Marco Stenta
- Syngenta Crop Protection AG, Schaffhauserstrasse, 4332, Stein, AG, Switzerland
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Lim PK, Julca I, Mutwil M. Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data. Comput Struct Biotechnol J 2023; 21:1639-1650. [PMID: 36874159 PMCID: PMC9976193 DOI: 10.1016/j.csbj.2023.01.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
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Affiliation(s)
- Peng Ken Lim
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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8
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Kulczyk S, Koszytkowska-Stawińska M. Novel drug design framework as a response to neglected and emerging diseases. J Biomol Struct Dyn 2022:1-12. [DOI: 10.1080/07391102.2022.2110519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Stanisław Kulczyk
- Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland
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