1
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Nana Teukam YG, Kwate Dassi L, Manica M, Probst D, Schwaller P, Laino T. Language models can identify enzymatic binding sites in protein sequences. Comput Struct Biotechnol J 2024; 23:1929-1937. [PMID: 38736695 PMCID: PMC11087710 DOI: 10.1016/j.csbj.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 05/14/2024] Open
Abstract
Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.
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Affiliation(s)
| | - Loïc Kwate Dassi
- IBM Research Europe, Saümerstrasse 4, 8803 Rüschlikon, Switzerland
| | - Matteo Manica
- IBM Research Europe, Saümerstrasse 4, 8803 Rüschlikon, Switzerland
| | - Daniel Probst
- IBM Research Europe, Saümerstrasse 4, 8803 Rüschlikon, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), Switzerland
| | - Philippe Schwaller
- IBM Research Europe, Saümerstrasse 4, 8803 Rüschlikon, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), Switzerland
| | - Teodoro Laino
- IBM Research Europe, Saümerstrasse 4, 8803 Rüschlikon, Switzerland
- National Center for Competence in Research-Catalysis (NCCR-Catalysis), Switzerland
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2
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Zhang C, Zhai Y, Gong Z, Duan H, She YB, Yang YF, Su A. Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data. J Cheminform 2024; 16:89. [PMID: 39080777 PMCID: PMC11290278 DOI: 10.1186/s13321-024-00886-1] [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: 03/05/2024] [Accepted: 07/21/2024] [Indexed: 08/02/2024] Open
Abstract
Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.Scientific contributionThis study verifies the feasibility of applying transfer learning to large language models in different chemical fields to help organic materials perform virtual screening. Through the comparison of transfer learning from different chemical fields to a variety of organic material molecules, the high precision virtual screening of organic materials is realized.
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Affiliation(s)
- Chengwei Zhang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - Yushuang Zhai
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - Ziyang Gong
- Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, People's Republic of China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
| | - Yuan-Bin She
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - Yun-Fang Yang
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China
| | - An Su
- State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China.
- Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, People's Republic of China.
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3
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Wiest O, Bauer C, Helquist P, Norrby PO, Genheden S. Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions. J Chem Inf Model 2024. [PMID: 38995078 DOI: 10.1021/acs.jcim.4c00370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.
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Affiliation(s)
- Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Christoph Bauer
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden
| | - Paul Helquist
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden
| | - Samuel Genheden
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden
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4
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Luong KD, Singh A. Application of Transformers in Cheminformatics. J Chem Inf Model 2024; 64:4392-4409. [PMID: 38815246 PMCID: PMC11167597 DOI: 10.1021/acs.jcim.3c02070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/05/2024] [Accepted: 05/06/2024] [Indexed: 06/01/2024]
Abstract
By accelerating time-consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing methods that can discover patterns within chemical data and utilize this knowledge for a wide variety of downstream tasks, such as property prediction or substance generation. The complex and diverse chemical space requires complex machine learning architectures with great learning power. Recently, learning models based on transformer architectures have revolutionized multiple domains of machine learning, including natural language processing and computer vision. Naturally, there have been ongoing endeavors in adopting these techniques to the chemical domain, resulting in a surge of publications within a short period. The diversity of chemical structures, use cases, and learning models necessitate a comprehensive summarization of existing works. In this paper, we review recent innovations in adapting transformers to solve learning problems in chemistry. Because chemical data is diverse and complex, we structure our discussion based on chemical representations. Specifically, we highlight the strengths and weaknesses of each representation, the current progress of adapting transformer architectures, and future directions.
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Affiliation(s)
- Kha-Dinh Luong
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, United States
| | - Ambuj Singh
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, United States
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5
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Das M, Ghosh A, Sunoj RB. Advances in machine learning with chemical language models in molecular property and reaction outcome predictions. J Comput Chem 2024; 45:1160-1176. [PMID: 38299229 DOI: 10.1002/jcc.27315] [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: 11/22/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 02/02/2024]
Abstract
Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.
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Affiliation(s)
- Manajit Das
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, India
| | - Ankit Ghosh
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, India
| | - Raghavan B Sunoj
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, India
- Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Mumbai, India
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6
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M. Bran A, Cox S, Schilter O, Baldassari C, White AD, Schwaller P. Augmenting large language models with chemistry tools. NAT MACH INTELL 2024; 6:525-535. [PMID: 38799228 PMCID: PMC11116106 DOI: 10.1038/s42256-024-00832-8] [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: 09/13/2023] [Accepted: 03/27/2024] [Indexed: 05/29/2024]
Abstract
Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.
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Affiliation(s)
- Andres M. Bran
- Laboratory of Artificial Chemical Intelligence (LIAC), ISIC, EPFL, Lausanne, Switzerland
- National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland
| | - Sam Cox
- Department of Chemical Engineering, University of Rochester, Rochester, NY USA
- FutureHouse, San Francisco, CA USA
| | - Oliver Schilter
- Laboratory of Artificial Chemical Intelligence (LIAC), ISIC, EPFL, Lausanne, Switzerland
- National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland
- Accelerated Discovery, IBM Research – Europe, Rüschlikon, Switzerland
| | - Carlo Baldassari
- Accelerated Discovery, IBM Research – Europe, Rüschlikon, Switzerland
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, NY USA
- FutureHouse, San Francisco, CA USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC), ISIC, EPFL, Lausanne, Switzerland
- National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland
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7
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Westerlund AM, Manohar Koki S, Kancharla S, Tibo A, Saigiridharan L, Kabeshov M, Mercado R, Genheden S. Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis. J Chem Inf Model 2024; 64:3021-3033. [PMID: 38602390 DOI: 10.1021/acs.jcim.3c01685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called the Chemformer, for retrosynthesis predictions within drug discovery. The proprietary data set used for training comprised ∼18 M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multistep retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to that of a template-based model. By analyzing multistep retrosynthesis experiments, we observed that Chemformer found synthetic routes, leading to commercial starting materials for 95% of the target compounds, an increase of more than 20% compared to the template-based model on a proprietary compound data set. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates, which are not included in the template-based model. These findings were further supported by a publicly available ChEMBL compound data set. The conclusions drawn from this work allow for the design of a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.
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Affiliation(s)
- Annie M Westerlund
- Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
| | - Siva Manohar Koki
- Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Supriya Kancharla
- Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Alessandro Tibo
- Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
| | | | - Mikhail Kabeshov
- Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
| | - Rocío Mercado
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Samuel Genheden
- Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden
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8
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Ding Y, Qiang B, Chen Q, Liu Y, Zhang L, Liu Z. Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective. J Chem Inf Model 2024; 64:2955-2970. [PMID: 38489239 DOI: 10.1021/acs.jcim.4c00004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.
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Affiliation(s)
- Yuheng Ding
- Department of Pharmaceutical Science, Peking University, Beijing 100191, China
| | - Bo Qiang
- Department of Pharmaceutical Science, Peking University, Beijing 100191, China
| | - Qixuan Chen
- Department of Pharmaceutical Science, Peking University, Beijing 100191, China
| | - Yiqiao Liu
- Department of Pharmaceutical Science, Peking University, Beijing 100191, China
| | - Liangren Zhang
- Department of Pharmaceutical Science, Peking University, Beijing 100191, China
| | - Zhenming Liu
- Department of Pharmaceutical Science, Peking University, Beijing 100191, China
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9
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Yao S, Song J, Jia L, Cheng L, Zhong Z, Song M, Feng Z. Fast and effective molecular property prediction with transferability map. Commun Chem 2024; 7:85. [PMID: 38632308 PMCID: PMC11024153 DOI: 10.1038/s42004-024-01169-4] [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: 11/05/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive training on target tasks. To quantify transferability concerning task-relatedness, we propose Principal Gradient-based Measurement (PGM) for transferring molecular property prediction ability. First, we design an optimization-free scheme to calculate a principal gradient for approximating the direction of model optimization on a molecular property prediction dataset. We have analyzed the close connection between the principal gradient and model optimization through mathematical proof. PGM measures the transferability as the distance between the principal gradient obtained from the source dataset and that derived from the target dataset. Then, we perform PGM on various molecular property prediction datasets to build a quantitative transferability map for source dataset selection. Finally, we evaluate PGM on multiple combinations of transfer learning tasks across 12 benchmark molecular property prediction datasets and demonstrate that it can serve as fast and effective guidance to improve the performance of a target task. This work contributes to more efficient discovery of drugs, materials, and catalysts by offering a task-relatedness quantification prior to transfer learning and understanding the relationship between chemical properties.
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Affiliation(s)
- Shaolun Yao
- Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, Zhejiang University, 310027, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Shanghai Institute for Advanced Study of Zhejiang University, 201203, Shanghai, China
| | - Jie Song
- Shanghai Institute for Advanced Study of Zhejiang University, 201203, Shanghai, China
- School of Software Technology, Zhejiang University, 315048, Ningbo, China
| | - Lingxiang Jia
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
| | - Lechao Cheng
- School of Computer Science and Information Engineering, Hefei University of Technology, 230009, Hefei, China
| | - Zipeng Zhong
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
| | - Mingli Song
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Shanghai Institute for Advanced Study of Zhejiang University, 201203, Shanghai, China
| | - Zunlei Feng
- Shanghai Institute for Advanced Study of Zhejiang University, 201203, Shanghai, China.
- School of Software Technology, Zhejiang University, 315048, Ningbo, China.
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10
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Qian W, Wang X, Kang Y, Pan P, Hou T, Hsieh CY. A general model for predicting enzyme functions based on enzymatic reactions. J Cheminform 2024; 16:38. [PMID: 38556873 PMCID: PMC10983695 DOI: 10.1186/s13321-024-00827-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/16/2024] [Indexed: 04/02/2024] Open
Abstract
Accurate prediction of the enzyme comission (EC) numbers for chemical reactions is essential for the understanding and manipulation of enzyme functions, biocatalytic processes and biosynthetic planning. A number of machine leanring (ML)-based models have been developed to classify enzymatic reactions, showing great advantages over costly and long-winded experimental verifications. However, the prediction accuracy for most available models trained on the records of chemical reactions without specifying the enzymatic catalysts is rather limited. In this study, we introduced BEC-Pred, a BERT-based multiclassification model, for predicting EC numbers associated with reactions. Leveraging transfer learning, our approach achieves precise forecasting across a wide variety of Enzyme Commission (EC) numbers solely through analysis of the SMILES sequences of substrates and products. BEC-Pred model outperformed other sequence and graph-based ML methods, attaining a higher accuracy of 91.6%, surpassing them by 5.5%, and exhibiting superior F1 scores with improvements of 6.6% and 6.0%, respectively. The enhanced performance highlights the potential of BEC-Pred to serve as a reliable foundational tool to accelerate the cutting-edge research in synthetic biology and drug metabolism. Moreover, we discussed a few examples on how BEC-Pred could accurately predict the enzymatic classification for the Novozym 435-induced hydrolysis and lipase efficient catalytic synthesis. We anticipate that BEC-Pred will have a positive impact on the progression of enzymatic research.
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Affiliation(s)
- Wenjia Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Xiaorui Wang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, 999078, China
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, 310018, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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11
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Liu Y, Liu X, Cao B. Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2024; 36:215901. [PMID: 38306704 DOI: 10.1088/1361-648x/ad2584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Bringing advances in machine learning to chemical science is leading to a revolutionary change in the way of accelerating materials discovery and atomic-scale simulations. Currently, most successful machine learning schemes can be largely traced to the use of localized atomic environments in the structural representation of materials and molecules. However, this may undermine the reliability of machine learning models for mapping complex systems and describing long-range physical effects because of the lack of non-local correlations between atoms. To overcome such limitations, here we report a graph attention neural network as a unified framework to map materials and molecules into a generalizable and interpretable representation that combines local and non-local information of atomic environments from multiple scales. As an exemplary study, our model is applied to predict the electronic structure properties of metal-organic frameworks (MOFs) which have notable diversity in compositions and structures. The results show that our model achieves the state-of-the-art performance. The clustering analysis further demonstrates that our model enables high-level identification of MOFs with spatial and chemical resolution, which would facilitate the rational design of promising reticular materials. Furthermore, the application of our model in predicting the heat capacity of complex nanoporous materials, a critical property in a carbon capture process, showcases its versatility and accuracy in handling diverse physical properties beyond electronic structures.
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Affiliation(s)
- Yuanbin Liu
- Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, People's Republic of China
- Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford, OX1 3QR, United Kingdom
| | - Xin Liu
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
- Key Laboratory of Engineering Dielectric and Applications of Ministry of Education, School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, People's Republic of China
| | - Bingyang Cao
- Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, People's Republic of China
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12
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Ayres LB, Gomez FJV, Silva MF, Linton JR, Garcia CD. Predicting the formation of NADES using a transformer-based model. Sci Rep 2024; 14:2715. [PMID: 38388549 PMCID: PMC10883925 DOI: 10.1038/s41598-022-27106-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/26/2022] [Indexed: 02/24/2024] Open
Abstract
The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.
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Affiliation(s)
- Lucas B Ayres
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA
| | - Federico J V Gomez
- Facultad de Ciencias Agrarias, Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Maria Fernanda Silva
- Facultad de Ciencias Agrarias, Instituto de Biología Agrícola de Mendoza (IBAM-CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
| | - Jeb R Linton
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA
- IBM Cloud, Armonk, NY, 10504, USA
| | - Carlos D Garcia
- Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA.
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13
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Carracedo-Reboredo P, Aranzamendi E, He S, Arrasate S, Munteanu CR, Fernandez-Lozano C, Sotomayor N, Lete E, González-Díaz H. MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products. J Cheminform 2024; 16:9. [PMID: 38254200 PMCID: PMC10804835 DOI: 10.1186/s13321-024-00802-7] [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: 03/01/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Eider Aranzamendi
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Shan He
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Cristian R Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Nuria Sotomayor
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Esther Lete
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.
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14
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Back S, Aspuru-Guzik A, Ceriotti M, Gryn'ova G, Grzybowski B, Gu GH, Hein J, Hippalgaonkar K, Hormázabal R, Jung Y, Kim S, Kim WY, Moosavi SM, Noh J, Park C, Schrier J, Schwaller P, Tsuda K, Vegge T, von Lilienfeld OA, Walsh A. Accelerated chemical science with AI. DIGITAL DISCOVERY 2024; 3:23-33. [PMID: 38239898 PMCID: PMC10793638 DOI: 10.1039/d3dd00213f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/06/2023] [Indexed: 01/22/2024]
Abstract
In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.
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Affiliation(s)
- Seoin Back
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University Seoul Republic of Korea
| | - Alán Aspuru-Guzik
- Departments of Chemistry, Computer Science, University of Toronto St. George Campus Toronto ON Canada
- Acceleration Consortium and Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Ganna Gryn'ova
- Heidelberg Institute for Theoretical Studies (HITS gGmbH) 69118 Heidelberg Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University 69120 Heidelberg Germany
| | - Bartosz Grzybowski
- Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS) Ulsan Republic of Korea
- Institute of Organic Chemistry, Polish Academy of Sciences Warsaw Poland
- Department of Chemistry, Ulsan National Institute of Science and Technology Ulsan Republic of Korea
| | - Geun Ho Gu
- Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH) Naju 58330 Republic of Korea
| | - Jason Hein
- Department of Chemistry, University of British Columbia Vancouver BC V6T 1Z1 Canada
| | - Kedar Hippalgaonkar
- School of Materials Science and Engineering, Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
- Institute of Materials Research and Engineering, Agency for Science Technology and Research 2 Fusionopolis Way, 08-03 Singapore 138634 Singapore
| | | | - Yousung Jung
- Department of Chemical and Biomolecular Engineering, KAIST Daejeon Republic of Korea
- School of Chemical and Biological Engineering, Interdisciplinary Program in Artificial Intelligence, Seoul National University 1 Gwanak-ro, Gwanak-gu Seoul 08826 Republic of Korea
| | - Seonah Kim
- Department of Chemistry, Colorado State University 1301 Center Avenue Fort Collins CO 80523 USA
| | - Woo Youn Kim
- Department of Chemistry, KAIST Daejeon Republic of Korea
| | - Seyed Mohamad Moosavi
- Chemical Engineering & Applied Chemistry, University of Toronto Toronto Ontario M5S 3E5 Canada
| | - Juhwan Noh
- Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology Daejeon 34114 Republic of Korea
| | | | - Joshua Schrier
- Department of Chemistry, Fordham University The Bronx NY 10458 USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC) & National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Koji Tsuda
- Graduate School of Frontier Sciences, The University of Tokyo Kashiwa Chiba 277-8561 Japan
- Center for Basic Research on Materials, National Institute for Materials Science Tsukuba Ibaraki 305-0044 Japan
- RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark 301 Anker Engelunds vej, Kongens Lyngby Copenhagen 2800 Denmark
| | - O Anatole von Lilienfeld
- Acceleration Consortium and Vector Institute for Artificial Intelligence Toronto ON M5S 1M1 Canada
- Departments of Chemistry, Materials Science and Engineering, and Physics, University of Toronto, St George Campus Toronto ON Canada
- Machine Learning Group, Technische Universität Berlin and Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany
| | - Aron Walsh
- Department of Materials, Imperial College London London SW7 2AZ UK
- Department of Physics, Ewha Women's University Seoul Republic of Korea
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15
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Voinarovska V, Kabeshov M, Dudenko D, Genheden S, Tetko IV. When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges. J Chem Inf Model 2024; 64:42-56. [PMID: 38116926 PMCID: PMC10778086 DOI: 10.1021/acs.jcim.3c01524] [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: 09/21/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023]
Abstract
Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.
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Affiliation(s)
- Varvara Voinarovska
- Molecular
AI, Discovery Sciences R&D, AstraZeneca, 431 83 Gothenburg, Sweden
- TUM
Graduate School, Faculty of Chemistry, Technical
University of Munich, 85748 Garching, Germany
| | - Mikhail Kabeshov
- Molecular
AI, Discovery Sciences R&D, AstraZeneca, 431 83 Gothenburg, Sweden
| | - Dmytro Dudenko
- Enamine
Ltd., 78 Chervonotkatska str., 02094 Kyiv, Ukraine
| | - Samuel Genheden
- Molecular
AI, Discovery Sciences R&D, AstraZeneca, 431 83 Gothenburg, Sweden
| | - Igor V. Tetko
- Molecular
Targets and Therapeutics Center, Helmholtz Munich − Deutsches
Forschungszentrum für Gesundheit und Umwelt (GmbH), Institute of Structural Biology, 85764 Neuherberg, Germany
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16
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Liu T, Cao Z, Huang Y, Wan Y, Wu J, Hsieh CY, Hou T, Kang Y. SynCluster: Reaction Type Clustering and Recommendation Framework for Synthesis Planning. JACS AU 2023; 3:3446-3461. [PMID: 38155655 PMCID: PMC10751778 DOI: 10.1021/jacsau.3c00607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 12/30/2023]
Abstract
AI-assisted synthesis planning has emerged as a valuable tool in accelerating synthetic chemistry for the discovery of new drugs and materials. The template-free approach, which showcases superior generalization capabilities, is seen as the mainstream direction in this field. However, it remains unclear whether such an end-to-end approach can achieve problem-solving performance on par with experienced chemists without fully revealing insights into the chemical mechanisms involved. Moreover, there is a lack of unified and chemically inspired frameworks for improving multitask reaction predictions in this area. In this study, we have addressed these challenges by investigating the impact of fine-grained reaction-type labels on multiple downstream tasks and propose a novel framework named SynCluster. This framework incorporates unsupervised clustering cues into the baseline models and identifies plausible chemical subspaces which is compatible with multitask extensions and can serve as model-independent indicators to effectively enhance the performance of multiple downstream tasks. In retrosynthesis prediction, SynCluster achieves significant improvements of 4.1 and 11.0% in top-1 and top-10 prediction accuracy, respectively, compared to the baseline Molecular Transformer, and achieves a notable enhancement of 13.9% in top-10 accuracy when combined with Retroformer. By incorporating simplified molecular-input line-entry system augmentation, our framework achieves higher top-10 accuracy compared to state-of-the-art sequence-based retrosynthesis models and improves over the baseline on the diversity and validity of reactants. SynCluster also achieves 94.9% top-10 accuracy in forward synthesis prediction and 51.5% top-10 Maxfrag accuracy in reagent prediction. Overall, SynCluster provides a fresh perspective with chemical interpretability and reinforcement of domain knowledge in the synthesis design. It offers a promising solution for improving the accuracy and efficiency of AI-assisted synthesis planning and bridges the gap between template-free approaches and the problem-solving abilities of experienced chemists.
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Affiliation(s)
- Tiantao Liu
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zheng Cao
- College
of Computer Science and Technology, Zhejiang
University, Hangzhou 310027, Zhejiang, China
| | - Yuansheng Huang
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yue Wan
- Tencent
Quantum Laboratory, Shenzhen 518057, Guangdong, China
| | - Jian Wu
- Second
Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation
Institute for Artificial Intelligence in Medicine of Zhejiang University,
College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China
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17
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McGibbon M, Shave S, Dong J, Gao Y, Houston DR, Xie J, Yang Y, Schwaller P, Blay V. From intuition to AI: evolution of small molecule representations in drug discovery. Brief Bioinform 2023; 25:bbad422. [PMID: 38033290 PMCID: PMC10689004 DOI: 10.1093/bib/bbad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.
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Affiliation(s)
- Miles McGibbon
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Steven Shave
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China
| | - Yumiao Gao
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jiancong Xie
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Yuedong Yang
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vincent Blay
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
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18
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Wang Z, Feng Z, Li Y, Li B, Wang Y, Sha C, He M, Li X. BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation. Brief Bioinform 2023; 25:bbad400. [PMID: 38033291 PMCID: PMC10783874 DOI: 10.1093/bib/bbad400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023] Open
Abstract
Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.
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Affiliation(s)
- Zhen Wang
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan, China
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, Zhejiang, China
| | - Zheng Feng
- Department of Health Outcomes & Biomedical Informatics, College of Medecine, University of Florida, Gainesville, 32611, FL, USA
| | - Yanjun Li
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, 32610, FL, USA
- Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, 32610, FL, USA
| | - Bowen Li
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, Zhejiang, China
| | - Yongrui Wang
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, Zhejiang, China
| | - Chulin Sha
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, Zhejiang, China
| | - Min He
- College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan, China
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, Zhejiang, China
| | - Xiaolin Li
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, Zhejiang, China
- ElasticMind Inc, Hangzhou, 310018, Zhejiang, China
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19
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Toniato A, Vaucher AC, Lehmann MM, Luksch T, Schwaller P, Stenta M, Laino T. Fast Customization of Chemical Language Models to Out-of-Distribution Data Sets. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:8806-8815. [PMID: 38027545 PMCID: PMC10653079 DOI: 10.1021/acs.chemmater.3c01406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023]
Abstract
The world is on the verge of a new industrial revolution, and language models are poised to play a pivotal role in this transformative era. Their ability to offer intelligent insights and forecasts has made them a valuable asset for businesses seeking a competitive advantage. The chemical industry, in particular, can benefit significantly from harnessing their power. Since 2016 already, language models have been applied to tasks such as predicting reaction outcomes or retrosynthetic routes. While such models have demonstrated impressive abilities, the lack of publicly available data sets with universal coverage is often the limiting factor for achieving even higher accuracies. This makes it imperative for organizations to incorporate proprietary data sets into their model training processes to improve their performance. So far, however, these data sets frequently remain untapped as there are no established criteria for model customization. In this work, we report a successful methodology for retraining language models on reaction outcome prediction and single-step retrosynthesis tasks, using proprietary, nonpublic data sets. We report a considerable boost in accuracy by combining patent and proprietary data in a multidomain learning formulation. This exercise, inspired by a real-world use case, enables us to formulate guidelines that can be adopted in different corporate settings to customize chemical language models easily.
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Affiliation(s)
- Alessandra Toniato
- IBM
Research Europe, Rüschlikon 8803, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | - Alain C. Vaucher
- IBM
Research Europe, Rüschlikon 8803, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | | | | | - Philippe Schwaller
- IBM
Research Europe, Rüschlikon 8803, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | - Marco Stenta
- Syngenta
Crop Protection AG, Stein 4332, Switzerland
| | - Teodoro Laino
- IBM
Research Europe, Rüschlikon 8803, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
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20
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Shilpa S, Kashyap G, Sunoj RB. Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions. J Phys Chem A 2023; 127:8253-8271. [PMID: 37769193 DOI: 10.1021/acs.jpca.3c04779] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of molecular property prediction (MPP) and chemical reaction prediction (CRP), herein we highlight some of its most recent applications in predictive chemistry. We present a nonmathematical and concise overview of the progression of ML implementations, ranging from an ensemble-based random forest model to advanced graph neural network algorithms. Similarly, the prospects of various feature engineering and feature learning approaches that work in conjunction with ML models are described. Highly accurate predictions reported in MPP tasks (e.g., lipophilicity, solubility, distribution coefficient), using methods such as D-MPNN, MolCLR, SMILES-BERT, and MolBERT, offer promising avenues in molecular design and drug discovery. Whereas MPP pertains to a given molecule, ML applications in chemical reactions present a different level of challenge, primarily arising from the simultaneous involvement of multiple molecules and their diverse roles in a reaction setting. The reported RMSEs in MPP tasks range from 0.287 to 2.20, while those for yield predictions are well over 4.9 in the lower end, reaching thresholds of >10.0 in several examples. Our Review concludes with a set of persisting challenges in dealing with reaction data sets and an overall optimistic outlook on benefits of ML-driven workflows for various MPP as well as CRP tasks.
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Affiliation(s)
- Shilpa Shilpa
- Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Gargee Kashyap
- Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Raghavan B Sunoj
- Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
- Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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21
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Hillers-Bendtsen AE, Elholm JL, Obel OB, Hölzel H, Moth-Poulsen K, Mikkelsen KV. Searching the Chemical Space of Bicyclic Dienes for Molecular Solar Thermal Energy Storage Candidates. Angew Chem Int Ed Engl 2023; 62:e202309543. [PMID: 37489860 DOI: 10.1002/anie.202309543] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 07/26/2023]
Abstract
Photoswitches are molecular systems that are chemically transformed subsequent to interaction with light and they find potential application in many new technologies. The design and discovery of photoswitch candidates require intricate molecular engineering of a range of properties to optimize a candidate to a specific applications, a task which can be tackled efficiently using quantum chemical screening procedures. In this paper, we perform a large scale screening of approximately half a million bicyclic diene photoswitches in the context of molecular solar thermal energy storage using ab initio quantum chemical methods. We further device an efficient strategy for scoring the systems based on their predicted solar energy conversion efficiency and elucidate potential pitfalls of this approach. Our search through the chemical space of bicyclic dienes reveals systems with unprecedented solar energy conversion efficiencies and storage densities that show promising design guidelines for next generation molecular solar thermal energy storage systems.
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Affiliation(s)
| | - Jacob Lynge Elholm
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen Ø, Denmark
- The Institute of Materials Science of Barcelona, ICMAB-CSIC, 08193, Bellaterra, Barcelona, Spain
| | - Oscar Berlin Obel
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen Ø, Denmark
| | - Helen Hölzel
- Department of Chemical Engineering, Universitat Politècnica de Catalunya, EEBE, Eduard Maristany 10-14, 08019, Barcelona, Spain
| | - Kasper Moth-Poulsen
- Department of Chemical Engineering, Universitat Politècnica de Catalunya, EEBE, Eduard Maristany 10-14, 08019, Barcelona, Spain
- The Institute of Materials Science of Barcelona, ICMAB-CSIC, 08193, Bellaterra, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, ICREA, Pg. Lluís Companys 23, Barcelona, Spain
- Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden
| | - Kurt V Mikkelsen
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen Ø, Denmark
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22
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Kreutter D, Reymond JL. Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search. Chem Sci 2023; 14:9959-9969. [PMID: 37736648 PMCID: PMC10510629 DOI: 10.1039/d3sc01604h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/30/2023] [Indexed: 09/23/2023] Open
Abstract
Computer-aided synthesis planning (CASP) aims to automatically learn organic reactivity from literature and perform retrosynthesis of unseen molecules. CASP systems must learn reactions sufficiently precisely to propose realistic disconnections, while avoiding overfitting to leave room for diverse options, and explore possible routes such as to allow short synthetic sequences to emerge. Herein we report an open-source CASP tool proposing original solutions to both challenges. First, we use a triple transformer loop (TTL) predicting starting materials (T1), reagents (T2), and products (T3) to explore various disconnection sites defined by combining systematic, template-based, and transformer-based tagging procedures. Second, we integrate TTL into a multistep tree search algorithm (TTLA) prioritizing sequences using a route penalty score (RPScore) considering the number of steps, their confidence score, and the simplicity of all intermediates along the route. Our approach favours short synthetic routes to commercial starting materials, as exemplified by retrosynthetic analyses of recently approved drugs.
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Affiliation(s)
- David Kreutter
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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23
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Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, Chandak P, Liu S, Van Katwyk P, Deac A, Anandkumar A, Bergen K, Gomes CP, Ho S, Kohli P, Lasenby J, Leskovec J, Liu TY, Manrai A, Marks D, Ramsundar B, Song L, Sun J, Tang J, Veličković P, Welling M, Zhang L, Coley CW, Bengio Y, Zitnik M. Scientific discovery in the age of artificial intelligence. Nature 2023; 620:47-60. [PMID: 37532811 DOI: 10.1038/s41586-023-06221-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/16/2023] [Indexed: 08/04/2023]
Abstract
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
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Affiliation(s)
- Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Tianfan Fu
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Wenhao Gao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziming Liu
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Shengchao Liu
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Peter Van Katwyk
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Andreea Deac
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- NVIDIA, Santa Clara, CA, USA
| | - Karianne Bergen
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Carla P Gomes
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Shirley Ho
- Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Physics and Center for Data Science, New York University, New York, NY, USA
| | | | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Arjun Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Le Song
- BioMap, Beijing, China
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jian Tang
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- HEC Montréal, Montreal, Quebec, Canada
- CIFAR AI Chair, Toronto, Ontario, Canada
| | - Petar Veličković
- Google DeepMind, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Max Welling
- University of Amsterdam, Amsterdam, Netherlands
- Microsoft Research Amsterdam, Amsterdam, Netherlands
| | - Linfeng Zhang
- DP Technology, Beijing, China
- AI for Science Institute, Beijing, China
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA.
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24
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Thakkar A, Vaucher AC, Byekwaso A, Schwaller P, Toniato A, Laino T. Unbiasing Retrosynthesis Language Models with Disconnection Prompts. ACS CENTRAL SCIENCE 2023; 9:1488-1498. [PMID: 37529205 PMCID: PMC10390024 DOI: 10.1021/acscentsci.3c00372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Indexed: 08/03/2023]
Abstract
Data-driven approaches to retrosynthesis are limited in user interaction, diversity of their predictions, and recommendation of unintuitive disconnection strategies. Herein, we extend the notions of prompt-based inference in natural language processing to the task of chemical language modeling. We show that by using a prompt describing the disconnection site in a molecule we can steer the model to propose a broader set of precursors, thereby overcoming training data biases in retrosynthetic recommendations and achieving a 39% performance improvement over the baseline. For the first time, the use of a disconnection prompt empowers chemists by giving them greater control over the disconnection predictions, which results in more diverse and creative recommendations. In addition, in place of a human-in-the-loop strategy, we propose a two-stage schema consisting of automatic identification of disconnection sites, followed by prediction of reactant sets, thereby achieving a considerable improvement in class diversity compared with the baseline. The approach is effective in mitigating prediction biases derived from training data. This provides a wider variety of usable building blocks and improves the end user's digital experience. We demonstrate its application to different chemistry domains, from traditional to enzymatic reactions, in which substrate specificity is critical.
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Affiliation(s)
- Amol Thakkar
- IBM
Research Europe, Saümerstrasse
4, 8803 Rüschlikon, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | - Alain C. Vaucher
- IBM
Research Europe, Saümerstrasse
4, 8803 Rüschlikon, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | - Andrea Byekwaso
- IBM
Research Europe, Saümerstrasse
4, 8803 Rüschlikon, Switzerland
| | - Philippe Schwaller
- IBM
Research Europe, Saümerstrasse
4, 8803 Rüschlikon, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | - Alessandra Toniato
- IBM
Research Europe, Saümerstrasse
4, 8803 Rüschlikon, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
| | - Teodoro Laino
- IBM
Research Europe, Saümerstrasse
4, 8803 Rüschlikon, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), 8093 Zürich, Switzerland
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25
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Shim E, Tewari A, Cernak T, Zimmerman PM. Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. J Chem Inf Model 2023; 63:3659-3668. [PMID: 37312524 PMCID: PMC11163943 DOI: 10.1021/acs.jcim.3c00577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.
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Affiliation(s)
- Eunjae Shim
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ambuj Tewari
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tim Cernak
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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26
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Li SW, Xu LC, Zhang C, Zhang SQ, Hong X. Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge. Nat Commun 2023; 14:3569. [PMID: 37322041 DOI: 10.1038/s41467-023-39283-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023] Open
Abstract
Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.
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Affiliation(s)
- Shu-Wen Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China
| | - Li-Cheng Xu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China
| | - Cheng Zhang
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Shuo-Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.
- Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street No. 2, Beijing, 100190, PR China.
- Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang Province, China.
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27
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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28
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Saebi M, Nan B, Herr JE, Wahlers J, Guo Z, Zurański AM, Kogej T, Norrby PO, Doyle AG, Chawla NV, Wiest O. On the use of real-world datasets for reaction yield prediction. Chem Sci 2023; 14:4997-5005. [PMID: 37206399 PMCID: PMC10189898 DOI: 10.1039/d2sc06041h] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/09/2023] [Indexed: 09/30/2023] Open
Abstract
The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.
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Affiliation(s)
- Mandana Saebi
- Department of Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame Notre Dame IN 46556 USA
| | - Bozhao Nan
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA
| | - John E Herr
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA
| | - Jessica Wahlers
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA
| | - Zhichun Guo
- Department of Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame Notre Dame IN 46556 USA
| | - Andrzej M Zurański
- Department of Chemistry, Princeton University Princeton New Jersey 08544 USA
| | - Thierry Kogej
- Molecular AI, Discovery Sciences, R&D, AstraZeneca Pepparedsleden 1, SE-431 83 Mölndal Gothenburg Sweden
| | - Per-Ola Norrby
- Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Pepparedsleden 1, SE-431 83 Mölndal Gothenburg Sweden
| | - Abigail G Doyle
- Department of Chemistry, Princeton University Princeton New Jersey 08544 USA
- Department of Chemistry and Biochemistry, University of California Los Angeles California 90095 USA
| | - Nitesh V Chawla
- Department of Computer Science and Engineering and Lucy Family Institute for Data and Society, University of Notre Dame Notre Dame IN 46556 USA
| | - Olaf Wiest
- Department of Chemistry and Biochemistry, University of Notre Dame Notre Dame IN 46556 USA
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29
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Yu T, Boob AG, Volk MJ, Liu X, Cui H, Zhao H. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal 2023. [DOI: 10.1038/s41929-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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30
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Lin MH, Wolf JB, Sletten ET, Cambié D, Danglad-Flores J, Seeberger PH. Enabling Technologies in Carbohydrate Chemistry: Automated Glycan Assembly, Flow Chemistry and Data Science. Chembiochem 2023; 24:e202200607. [PMID: 36382494 DOI: 10.1002/cbic.202200607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/15/2022] [Indexed: 11/17/2022]
Abstract
The synthesis of defined oligosaccharides is a complex task. Several enabling technologies have been introduced in the last two decades to facilitate synthetic access to these valuable biomolecules. In this concept, we describe the technological solutions that have advanced glycochemistry using automated glycan assembly, flow chemistry and data science as examples. We highlight how the synergies between these different technologies can further advance the field, with progress toward the realization of a self-driving lab for glycan synthesis.
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Affiliation(s)
- Mei-Huei Lin
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.,Department of Chemistry and Biochemistry, Freie Universität Berlin, Arnimallee 22, 14195, Berlin, Germany
| | - Jakob B Wolf
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.,Department of Chemistry and Biochemistry, Freie Universität Berlin, Arnimallee 22, 14195, Berlin, Germany
| | - Eric T Sletten
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Dario Cambié
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - José Danglad-Flores
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany
| | - Peter H Seeberger
- Department of Biomolecular Systems, Max-Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.,Department of Chemistry and Biochemistry, Freie Universität Berlin, Arnimallee 22, 14195, Berlin, Germany
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31
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Zhang SQ, Xu LC, Li SW, Oliveira JCA, Li X, Ackermann L, Hong X. Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis. Chemistry 2023; 29:e202202834. [PMID: 36206170 PMCID: PMC10099903 DOI: 10.1002/chem.202202834] [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: 09/12/2022] [Indexed: 11/29/2022]
Abstract
Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.
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Affiliation(s)
- Shuo-Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - Li-Cheng Xu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - Shu-Wen Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - João C A Oliveira
- Institut für Organische und Biomolekulare Chemie, Wöhler Research Institute for Sustainable Chemistry (WISCh), Georg-August-Universität, Tammannstraße 2, 37077, Göttingen, Germany
| | - Xin Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China
| | - Lutz Ackermann
- Institut für Organische und Biomolekulare Chemie, Wöhler Research Institute for Sustainable Chemistry (WISCh), Georg-August-Universität, Tammannstraße 2, 37077, Göttingen, Germany
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China.,Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street No. 2, Beijing, 100190, P. R. China.,Key Laboratory of Precise Synthesis of, Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang Province, P. R. China
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32
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A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0. Processes (Basel) 2023. [DOI: 10.3390/pr11020330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.
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33
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HS, an Ancient Molecular Recognition and Information Storage Glycosaminoglycan, Equips HS-Proteoglycans with Diverse Matrix and Cell-Interactive Properties Operative in Tissue Development and Tissue Function in Health and Disease. Int J Mol Sci 2023; 24:ijms24021148. [PMID: 36674659 PMCID: PMC9867265 DOI: 10.3390/ijms24021148] [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: 11/15/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 01/11/2023] Open
Abstract
Heparan sulfate is a ubiquitous, variably sulfated interactive glycosaminoglycan that consists of repeating disaccharides of glucuronic acid and glucosamine that are subject to a number of modifications (acetylation, de-acetylation, epimerization, sulfation). Variable heparan sulfate chain lengths and sequences within the heparan sulfate chains provide structural diversity generating interactive oligosaccharide binding motifs with a diverse range of extracellular ligands and cellular receptors providing instructional cues over cellular behaviour and tissue homeostasis through the regulation of essential physiological processes in development, health, and disease. heparan sulfate and heparan sulfate-PGs are integral components of the specialized glycocalyx surrounding cells. Heparan sulfate is the most heterogeneous glycosaminoglycan, in terms of its sequence and biosynthetic modifications making it a difficult molecule to fully characterize, multiple ligands also make an elucidation of heparan sulfate functional properties complicated. Spatio-temporal presentation of heparan sulfate sulfate groups is an important functional determinant in tissue development and in cellular control of wound healing and extracellular remodelling in pathological tissues. The regulatory properties of heparan sulfate are mediated via interactions with chemokines, chemokine receptors, growth factors and morphogens in cell proliferation, differentiation, development, tissue remodelling, wound healing, immune regulation, inflammation, and tumour development. A greater understanding of these HS interactive processes will improve therapeutic procedures and prognoses. Advances in glycosaminoglycan synthesis and sequencing, computational analytical carbohydrate algorithms and advanced software for the evaluation of molecular docking of heparan sulfate with its molecular partners are now available. These advanced analytic techniques and artificial intelligence offer predictive capability in the elucidation of heparan sulfate conformational effects on heparan sulfate-ligand interactions significantly aiding heparan sulfate therapeutics development.
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34
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Tu Z, Stuyver T, Coley CW. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery. Chem Sci 2023; 14:226-244. [PMID: 36743887 PMCID: PMC9811563 DOI: 10.1039/d2sc05089g] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.
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Affiliation(s)
- Zhengkai Tu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Thijs Stuyver
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Connor W Coley
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
- Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA
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35
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Su A, Zhang X, Zhang C, Ding D, Yang YF, Wang K, She YB. Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts. Phys Chem Chem Phys 2023; 25:10536-10549. [PMID: 36987933 DOI: 10.1039/d3cp00917c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
A deep transfer learning approach is used to predict HOMO/LUMO energies of organic materials with a small amount of training data.
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Affiliation(s)
- An Su
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Xin Zhang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Chengwei Zhang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Debo Ding
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Yun-Fang Yang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Keke Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
| | - Yuan-Bin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China.
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36
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Andronov M, Voinarovska V, Andronova N, Wand M, Clevert DA, Schmidhuber J. Reagent prediction with a molecular transformer improves reaction data quality. Chem Sci 2023; 14:3235-3246. [PMID: 36970100 PMCID: PMC10034139 DOI: 10.1039/d2sc06798f] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/12/2023] [Indexed: 03/05/2023] Open
Abstract
A molecular transformer predicts reagents for organic reactions. It is also able to replace questionable reagents in reaction data, e.g. USPTO, to enable better product prediction models to be trained on these new data.
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Affiliation(s)
- Mikhail Andronov
- IDSIA, USI, SUPSI, 6900 Lugano, Switzerland
- Machine Learning Research, Pfizer Worldwide Research Development and Medical, Linkstr.10, Berlin, Germany
| | - Varvara Voinarovska
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich – Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
| | | | - Michael Wand
- IDSIA, USI, SUPSI, 6900 Lugano, Switzerland
- Institute for Digital Technologies for Personalized Healthcare, SUPSI, 6900 Lugano, Switzerland
| | - Djork-Arné Clevert
- Machine Learning Research, Pfizer Worldwide Research Development and Medical, Linkstr.10, Berlin, Germany
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37
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Li G, Buric F, Zrimec J, Viknander S, Nielsen J, Zelezniak A, Engqvist MKM. Learning deep representations of enzyme thermal adaptation. Protein Sci 2022; 31:e4480. [PMID: 36261883 PMCID: PMC9679980 DOI: 10.1002/pro.4480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/02/2022] [Accepted: 10/15/2022] [Indexed: 12/14/2022]
Abstract
Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
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Affiliation(s)
- Gang Li
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Filip Buric
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Jan Zrimec
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden,Department of Biotechnology and Systems BiologyNational Institute of BiologyLjubljanaSlovenia
| | - Sandra Viknander
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden,BioInnovation InstituteCopenhagen NDenmark
| | - Aleksej Zelezniak
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden,Life Sciences CentreInstitute of Biotechnology, Vilnius UniversityVilniusLithuania,Randall Centre for Cell & Molecular BiophysicsKing's College London, New Hunt's House, Guy's Campus, SE1 1ULLondonUK
| | - Martin K. M. Engqvist
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden,Enginzyme ABStockholmSweden
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38
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Griffiths RR, Greenfield JL, Thawani AR, Jamasb AR, Moss HB, Bourached A, Jones P, McCorkindale W, Aldrick AA, Fuchter MJ, Lee AA. Data-driven discovery of molecular photoswitches with multioutput Gaussian processes. Chem Sci 2022; 13:13541-13551. [PMID: 36507171 PMCID: PMC9682911 DOI: 10.1039/d2sc04306h] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/16/2022] [Indexed: 11/11/2022] Open
Abstract
Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset.
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Affiliation(s)
- Ryan-Rhys Griffiths
- The Cavendish Laboratory, Department of Physics, University of CambridgeCambridge CB3 0HEUK
| | - Jake L. Greenfield
- Molecular Sciences Research Hub, Department of Chemistry, Imperial College LondonLondon W12 0BZUK,Center for Nanosystems Chemistry (CNC), Institut für Organische Chemie, Universität WürzburgWürzburg 97074Germany
| | - Aditya R. Thawani
- Molecular Sciences Research Hub, Department of Chemistry, Imperial College LondonLondon W12 0BZUK
| | - Arian R. Jamasb
- The Computer Laboratory, University of CambridgeCambridge CB3 0FDUK
| | | | - Anthony Bourached
- The Institute of Neurology, Department of Neurology, University College LondonLondon WC1N 3BGUK
| | - Penelope Jones
- The Cavendish Laboratory, Department of Physics, University of CambridgeCambridge CB3 0HEUK
| | - William McCorkindale
- The Cavendish Laboratory, Department of Physics, University of CambridgeCambridge CB3 0HEUK
| | - Alexander A. Aldrick
- The Cavendish Laboratory, Department of Physics, University of CambridgeCambridge CB3 0HEUK
| | - Matthew J. Fuchter
- Molecular Sciences Research Hub, Department of Chemistry, Imperial College LondonLondon W12 0BZUK
| | - Alpha A. Lee
- The Cavendish Laboratory, Department of Physics, University of CambridgeCambridge CB3 0HEUK
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39
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Zhao Z, Gui J, Yao A, Le NQK, Chua MCH. Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units. ACS OMEGA 2022; 7:40569-40577. [PMID: 36385847 PMCID: PMC9647964 DOI: 10.1021/acsomega.2c05881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
In recent times, the importance of peptides in the biomedical domain has received increasing concern in terms of their effect on multiple disease treatments. However, before successful large-scale implementation in the industry, accurate identification of peptide toxicity is a vital prerequisite. The existing computational methods have reached good results from toxicity prediction, and we present an improved model based on different deep learning architectures. The modification mainly focuses on two aspects: sequence encoding and variational information bottlenecks. Consequently, one of our modified plans shows an obvious increase in sensitivity, while the rest show good performance meanwhile adding novelty in the peptide toxicity prediction domain. In detail, our best model could achieve an accuracy of 97.38 and 95.03% in protein and peptide toxicity predictions, respectively. The performance was superior to previous predictors on the same datasets.
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Affiliation(s)
- Zhengyun Zhao
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| | - Jingyu Gui
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| | - Anqi Yao
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence
in Medicine, College of Medicine, Taipei
Medical University, Taipei 106, Taiwan
- Research
Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Matthew Chin Heng Chua
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
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40
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Yang J, Cai Y, Zhao K, Xie H, Chen X. Concepts and applications of chemical fingerprint for hit and lead screening. Drug Discov Today 2022; 27:103356. [PMID: 36113834 DOI: 10.1016/j.drudis.2022.103356] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022]
Abstract
Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.
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Affiliation(s)
- Jingbo Yang
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China
| | - Yiyang Cai
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China
| | - Kairui Zhao
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China
| | - Hongbo Xie
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China.
| | - Xiujie Chen
- Department of Pharmagenomics, College of Bioinformatics Science and Technology, Harbin Medical University, 150081 Harbin, Heilongjiang, China.
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41
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Wu X, Zhang Y, Yu J, Zhang C, Qiao H, Wu Y, Wang X, Wu Z, Duan H. Virtual data augmentation method for reaction prediction. Sci Rep 2022; 12:17098. [PMID: 36224331 PMCID: PMC9556613 DOI: 10.1038/s41598-022-21524-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 09/28/2022] [Indexed: 01/04/2023] Open
Abstract
To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.e., in the data analysis strategy, the fake data as compounds with modified functional groups to increase the amount of data for reaction prediction. This approach was tested on five different reactions, and the results show improvements over other relevant techniques with increased model predictivity. Furthermore, we evaluated this method in different models, confirming the generality of virtual data augmentation. In summary, virtual data augmentation can be used as an effective measure to solve the problem of insufficient data and significantly improve the performance of reaction prediction.
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Affiliation(s)
- Xinyi Wu
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Yun Zhang
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Jiahui Yu
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Chengyun Zhang
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Haoran Qiao
- grid.440635.00000 0000 9527 0839College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201203 People’s Republic of China
| | - Yejian Wu
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Xinqiao Wang
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Zhipeng Wu
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China
| | - Hongliang Duan
- grid.469325.f0000 0004 1761 325XArtificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014 People’s Republic of China ,grid.9227.e0000000119573309State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, 201203 People’s Republic of China
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42
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Wu Z, Cai X, Zhang C, Qiao H, Wu Y, Zhang Y, Wang X, Xie H, Luo F, Duan H. Self-Supervised Molecular Pretraining Strategy for Low-Resource Reaction Prediction Scenarios. J Chem Inf Model 2022; 62:4579-4590. [PMID: 36129104 DOI: 10.1021/acs.jcim.2c00588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problems. Using a self-supervised pretraining strategy called MAsked Sequence to Sequence (MASS), the Transformer model can absorb the chemical information of about 1 billion molecules and then fine-tune on a small-scale reaction prediction. To further strengthen the predictive performance of our model, we combine MASS with the reaction transfer learning strategy. Here, we show that the average improved accuracies of the Transformer model can reach 14.07, 24.26, 40.31, and 57.69% in predicting the Baeyer-Villiger, Heck, C-C bond formation, and functional group interconversion reaction data sets, respectively, marking an important step to low-resource reaction prediction.
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Affiliation(s)
- Zhipeng Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China
| | - Xiang Cai
- PyWise Biotech, Suzhou 215000, P. R. China
| | - Chengyun Zhang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China
| | - Haoran Qiao
- College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201203, P. R. China
| | - Yejian Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China
| | - Yun Zhang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China
| | - Xinqiao Wang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China
| | - Haiying Xie
- PUROTON Gene Medical Institute Co., Ltd., Chongqing 400700, P. R. China
| | - Feng Luo
- PUROTON Gene Medical Institute Co., Ltd., Chongqing 400700, P. R. China
| | - Hongliang Duan
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China
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43
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A generalized-template-based graph neural network for accurate organic reactivity prediction. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00526-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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44
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Yang G, Chiu WY, Wu J, Zhou Y, Chen S, Zhou W, Fan J, Chen G. Predicting Experimental Heats of Formation via Deep Learning with Limited Experimental Data. J Phys Chem A 2022; 126:6295-6300. [PMID: 36054912 DOI: 10.1021/acs.jpca.2c02957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
When it comes to predicting experimental values of molecular properties with deep learning, the key problem is the lack of sufficient experimental data for training. We propose a method that consists of pretraining a graph neural network that aims to reproduce first-principles quantum mechanical results, followed by fine-tuning of a fully connected neural network against experimental results. The combined pretraining and fine-tuning model is expected to yield molecular properties close to experimental accuracy. This is made possible because first-principles quantum mechanical methods are often qualitatively correct or semiquantitatively accurate; thus, a calibration of the calculation results against high-precision but limited experiment data can improve accuracy greatly. Moreover, the method is highly efficient, as first-principles quantum mechanical calculation is bypassed. To demonstrate this, we apply the combined model to determine the experimental heats of formation of organic molecules made of H, C, O, N, or F atoms (up to 30 atoms), where mere 405 experimental data are used. The overall mean absolute error is 1.8 kcal/mol for these molecules.
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Affiliation(s)
- GuanYa Yang
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Wai Yuet Chiu
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jiang Wu
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.,Hong Kong Quantum AI Lab Limited, Pak Shek Kok, Hong Kong SAR, China
| | - Yi Zhou
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - ShuGuang Chen
- Hong Kong Quantum AI Lab Limited, Pak Shek Kok, Hong Kong SAR, China
| | - WeiJun Zhou
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jiaqi Fan
- Hong Kong Quantum AI Lab Limited, Pak Shek Kok, Hong Kong SAR, China
| | - GuanHua Chen
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.,Hong Kong Quantum AI Lab Limited, Pak Shek Kok, Hong Kong SAR, China
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45
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Zheng S, Zeng T, Li C, Chen B, Coley CW, Yang Y, Wu R. Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP. Nat Commun 2022; 13:3342. [PMID: 35688826 PMCID: PMC9187661 DOI: 10.1038/s41467-022-30970-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 05/27/2022] [Indexed: 12/30/2022] Open
Abstract
The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs. The complete biosynthetic pathway from most natural products (NPs) are unknown. Here, the authors report BioNavi-NP, a computational toolkit for bio-retrosynthetic pathway elucidation or reconstruction for both NPs and NP-like compounds.
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Affiliation(s)
- Shuangjia Zheng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.,School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Galixir, Beijing, China.,School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Tao Zeng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China
| | | | - Binghong Chen
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
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46
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Shim E, Kammeraad JA, Xu Z, Tewari A, Cernak T, Zimmerman PM. Predicting reaction conditions from limited data through active transfer learning. Chem Sci 2022; 13:6655-6668. [PMID: 35756521 PMCID: PMC9172577 DOI: 10.1039/d1sc06932b] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 05/10/2022] [Indexed: 12/30/2022] Open
Abstract
Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small number of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning.
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Affiliation(s)
- Eunjae Shim
- Department of Chemistry, University of MichiganAnn ArborMIUSA
| | - Joshua A. Kammeraad
- Department of Chemistry, University of MichiganAnn ArborMIUSA,Department of Statistics, University of MichiganAnn ArborMIUSA
| | - Ziping Xu
- Department of Statistics, University of MichiganAnn ArborMIUSA
| | - Ambuj Tewari
- Department of Statistics, University of MichiganAnn ArborMIUSA,Department of Electrical Engineering and Computer Science, University of MichiganAnn ArborMIUSA
| | - Tim Cernak
- Department of Chemistry, University of MichiganAnn ArborMIUSA,Department of Medicinal Chemistry, University of MichiganAnn ArborMIUSA
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47
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End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks. BIOMEDICAL ENGINEERING ADVANCES 2022. [DOI: 10.1016/j.bea.2022.100038] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
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48
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Bender A, Schneider N, Segler M, Patrick Walters W, Engkvist O, Rodrigues T. Evaluation guidelines for machine learning tools in the chemical sciences. Nat Rev Chem 2022; 6:428-442. [PMID: 37117429 DOI: 10.1038/s41570-022-00391-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generation, improvement and/or ordering of research hypotheses. Despite the overarching applicability of ML workflows, one usually finds diverse evaluation study designs. The current heterogeneity in evaluation techniques and metrics leads to difficulty in (or the impossibility of) comparing and assessing the relevance of new algorithms. Ultimately, this may delay the digitalization of chemistry at scale and confuse method developers, experimentalists, reviewers and journal editors. In this Perspective, we critically discuss a set of method development and evaluation guidelines for different types of ML-based publications, emphasizing supervised learning. We provide a diverse collection of examples from various authors and disciplines in chemistry. While taking into account varying accessibility across research groups, our recommendations focus on reporting completeness and standardizing comparisons between tools. We aim to further contribute to improved ML transparency and credibility by suggesting a checklist of retro-/prospective tests and dissecting their importance. We envisage that the wide adoption and continuous update of best practices will encourage an informed use of ML on real-world problems related to the chemical sciences.
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Shafik W, Matinkhah SM, Shokoor F, Sharif L. A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation. EAI ENDORSED TRANSACTIONS ON INTERNET OF THINGS 2022. [DOI: 10.4108/eetiot.v8i29.987] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context. This is a comparison to supervised and non-supervised learning due to the interactive nature of the environment. Exploiting a forthcoming accumulative compensation and its stimulus of machines, complex policy decisions. The study further analyses and presents ML perspectives depicting state-of-the-art developments with advancement, relatively depicting the future trend of RL based on its applicability in technology. It's a challenge to an Internet of Things (IoT) and demonstrates what possibly can be adopted as a solution. This study presented a summarized perspective on identified arenas on the analysis of RL. The study scrutinized that a reasonable number of the techniques engrossed in alternating policy values instead of modifying other gears in an exact state of intellectual. The study presented a strong foundation for the current studies to be adopted by the researchers from different research backgrounds to develop models, and architectures that are relevant.
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Zeng T, Hess BA, Zhang F, Wu R. Bio-inspired chemical space exploration of terpenoids. Brief Bioinform 2022; 23:6586263. [PMID: 35576010 DOI: 10.1093/bib/bbac197] [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: 04/01/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/12/2022] Open
Abstract
Many computational methods are devoted to rapidly generating pseudo-natural products to expand the open-ended border of chemical spaces for natural products. However, the accessibility and chemical interpretation were often ignored or underestimated in conventional library/fragment-based or rule-based strategies, thus hampering experimental synthesis. Herein, a bio-inspired strategy (named TeroGen) is developed to mimic the two key biosynthetic stages (cyclization and decoration) of terpenoid natural products, by utilizing physically based simulations and deep learning models, respectively. The precision and efficiency are validated for different categories of terpenoids, and in practice, more than 30 000 sesterterpenoids (10 times as many as the known sesterterpenoids) are predicted to be linked in a reaction network, and their synthetic accessibility and chemical interpretation are estimated by thermodynamics and kinetics. Since it could not only greatly expand the chemical space of terpenoids but also numerate plausible biosynthetic routes, TeroGen is promising for accelerating heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids and derivatives.
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Affiliation(s)
- Tao Zeng
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P.R. China
| | | | - Fan Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P.R. China
| | - Ruibo Wu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, P.R. China
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