1
|
Zhai S, Tan Y, Zhu C, Zhang C, Gao Y, Mao Q, Zhang Y, Duan H, Yin Y. PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization. Eur J Med Chem 2024; 275:116628. [PMID: 38944933 DOI: 10.1016/j.ejmech.2024.116628] [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: 04/17/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 07/02/2024]
Abstract
Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.
Collapse
Affiliation(s)
- Silong Zhai
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Yahong Tan
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Cheng Zhu
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Chengyun Zhang
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Yan Gao
- Qilu Institute of Technology, Jinan, 250200, China
| | - Qingyi Mao
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Youming Zhang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
| | - Yizhen Yin
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China; Shandong Research Institute of Industrial Technology, Jinan, 250101, China.
| |
Collapse
|
2
|
An H, Liu X, Cai W, Shao X. AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology. J Chem Inf Model 2024. [PMID: 38982757 DOI: 10.1021/acs.jcim.4c00449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Rapid and accurate calculation of acid dissociation constant (pKa) is crucial for designing chemical synthesis routes, optimizing catalysts, and predicting chemical behavior. Despite recent progress in machine learning, predicting solvation acidity, especially in nonaqueous solvents, remains challenging due to limited experimental data. This challenge arises from treating experimental values in different solvents as distinct data domains and modeling them separately. In this work, we treat both the solutes and solvents equally from a perspective of molecular topology and propose a highly universal framework called AttenGpKa for predicting solvation acidity. AttenGpKa is trained using 26,522 experimental pKa values from 60 pure and mixed solvents in the iBonD database. As a result, our model can simultaneously predict the pKa values of a compound in various solvents, including pure water, pure nonaqueous, and mixed solvents. AttenGpKa achieves universality by using graph neural networks and attention mechanisms to learn complex effects within solute and solvent molecules. Furthermore, encodings of both solute and solvent molecules are adaptively fused to simulate the influence of the solvent on acid dissociation. AttenGpKa demonstrates robust generalization in extensive validations. The interpretability studies further indicate that our model has effectively learnt electronic and solvent effects. A free-to-use software is provided to facilitate the use of AttenGpKa for pKa prediction.
Collapse
Affiliation(s)
- Hongle An
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xuyang Liu
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| |
Collapse
|
3
|
Tin N, Chauhan M, Agwamba K, Sun Y, Parsons A, Payne P, Osan R. Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity. ACS OMEGA 2024; 9:28476-28484. [PMID: 38973894 PMCID: PMC11223213 DOI: 10.1021/acsomega.4c02427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024]
Abstract
The application of green chemistry is critical for cultivating environmental responsibility and sustainable practices in pharmaceutical manufacturing. Process mass intensity (PMI) is a key metric that quantifies the resource efficiency of a manufacturing process, but determining what constitutes a successful PMI of a specific molecule is challenging. A recent approach correlated molecular features to a crowdsourced definition of molecular complexity to determine PMI targets. While recent machine learning tools show promise in predicting molecular complexity, a more extensive application could significantly optimize manufacturing processes. To this end, we refine and expand upon the SMART-PMI tool by Sheridan et al. to create an open-source model and application. Our solution emphasizes explainability and parsimony to facilitate a nuanced understanding of prediction and ensure informed decision-making. The resulting model uses four descriptors-the heteroatom count, stereocenter count, unique topological torsion, and connectivity index chi4n-to compute molecular complexity with a comparable 82.6% predictive accuracy and 0.349 RMSE. We develop a corresponding app that takes in structured data files (SDF) to rapidly quantify molecular complexity and provide a PMI target that can be used to drive process development activities. By integrating machine learning explainability and open-source accessibility, we provide flexible tools to advance the field of green chemistry and sustainable pharmaceutical manufacturing.
Collapse
Affiliation(s)
- Nicole Tin
- Analytical
Sciences, Gilead Sciences Inc, Foster City, California 94404-1147, United
States
| | - Mandeep Chauhan
- Global
Quality Control Systems, Gilead Sciences
Inc, Foster City, California 94404-1147, United States
| | - Kennedy Agwamba
- Analytical
Sciences, Gilead Sciences Inc, Foster City, California 94404-1147, United
States
| | - Yibai Sun
- Process
Development, Gilead Alberta ULC, Edmonton, Alberta T6S 1A1, Canada
| | - Astrid Parsons
- Process
Development, Gilead Sciences Inc, Foster City, California 94404-1147, United
States
| | - Philippa Payne
- Global
External Manufacturing, Gilead Alberta ULC, Edmonton, Alberta T6S 1A1, Canada
| | - Remus Osan
- Analytical
Sciences, Gilead Sciences Inc, Foster City, California 94404-1147, United
States
| |
Collapse
|
4
|
Cardoso Rial R. AI in analytical chemistry: Advancements, challenges, and future directions. Talanta 2024; 274:125949. [PMID: 38569367 DOI: 10.1016/j.talanta.2024.125949] [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: 12/28/2023] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/05/2024]
Abstract
This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.
Collapse
Affiliation(s)
- Rafael Cardoso Rial
- Federal Institute of Mato Grosso do Sul, 79750-000, Nova Andradina, MS, Brazil.
| |
Collapse
|
5
|
Liu Q, He D, Fan M, Wang J, Cui Z, Wang H, Mi Y, Li N, Meng Q, Hou Y. Prediction and Interpretation Microglia Cytotoxicity by Machine Learning. J Chem Inf Model 2024. [PMID: 38949724 DOI: 10.1021/acs.jcim.4c00366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Ameliorating microglia-mediated neuroinflammation is a crucial strategy in developing new drugs for neurodegenerative diseases. Plant compounds are an important screening target for the discovery of drugs for the treatment of neurodegenerative diseases. However, due to the spatial complexity of phytochemicals, it becomes particularly important to evaluate the effectiveness of compounds while avoiding the mixing of cytotoxic substances in the early stages of compound screening. Traditional high-throughput screening methods suffer from high cost and low efficiency. A computational model based on machine learning provides a novel avenue for cytotoxicity determination. In this study, a microglia cytotoxicity classifier was developed using a machine learning approach. First, we proposed a data splitting strategy based on the molecule murcko generic scaffold, under this condition, three machine learning approaches were coupled with three kinds of molecular representation methods to construct microglia cytotoxicity classifier, which were then compared and assessed by the predictive accuracy, balanced accuracy, F1-score, and Matthews Correlation Coefficient. Then, the recursive feature elimination integrated with support vector machine (RFE-SVC) dimension reduction method was introduced to molecular fingerprints with high dimensions to further improve the model performance. Among all the microglial cytotoxicity classifiers, the SVM coupled with ECFP4 fingerprint after feature selection (ECFP4-RFE-SVM) obtained the most accurate classification for the test set (ACC of 0.99, BA of 0.99, F1-score of 0.99, MCC of 0.97). Finally, the Shapley additive explanations (SHAP) method was used in interpreting the microglia cytotoxicity classifier and key substructure smart identified as structural alerts. Experimental results show that ECFP4-RFE-SVM have reliable classification capability for microglia cytotoxicity, and SHAP can not only provide a rational explanation for microglia cytotoxicity predictions, but also offer a guideline for subsequent molecular cytotoxicity modifications.
Collapse
Affiliation(s)
- Qing Liu
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China
| | - Dakuo He
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China
| | - Mengmeng Fan
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China
| | - Jinpeng Wang
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China
| | - Zeyu Cui
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China
| | - Hao Wang
- College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China
| | - Yan Mi
- Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, P. R. China
| | - Ning Li
- School of Traditional Chinese Materia Medica, Key Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang City, Shenyang Pharmaceutical University, Shenyang 110016, P. R. China
| | - Qingqi Meng
- Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, P. R. China
| | - Yue Hou
- Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, P. R. China
| |
Collapse
|
6
|
Shen C, Song J, Hsieh CY, Cao D, Kang Y, Ye W, Wu Z, Wang J, Zhang O, Zhang X, Zeng H, Cai H, Chen Y, Chen L, Luo H, Zhao X, Jian T, Chen T, Jiang D, Wang M, Ye Q, Wu J, Du H, Shi H, Deng Y, Hou T. DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery. J Chem Inf Model 2024. [PMID: 38920405 DOI: 10.1021/acs.jcim.4c00621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.
Collapse
Affiliation(s)
- Chao Shen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jianfei Song
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Wenling Ye
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hao Zeng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Heng Cai
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yu Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Linkang Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Hao Luo
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Xinda Zhao
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tianye Jian
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Tong Chen
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Mingyang Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Qing Ye
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hui Shi
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tingjun Hou
- Hangzhou Carbonsilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| |
Collapse
|
7
|
Lavecchia A. Advancing drug discovery with deep attention neural networks. Drug Discov Today 2024; 29:104067. [PMID: 38925473 DOI: 10.1016/j.drudis.2024.104067] [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: 05/17/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.
Collapse
Affiliation(s)
- Antonio Lavecchia
- Drug Discovery Laboratory, Department of Pharmacy, University of Napoli Federico II, I-80131 Naples, Italy.
| |
Collapse
|
8
|
Taub R, Savir Y. SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery. J Chem Inf Model 2024; 64:4021-4030. [PMID: 38695490 PMCID: PMC11134513 DOI: 10.1021/acs.jcim.4c00107] [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: 01/19/2024] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 05/28/2024]
Abstract
Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as graphs and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom's information. Common aggregation operators, such as averaging, result in a loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted nonlinearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in β-lactam antibiotics. The ability of our approach to rank the atoms' importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level.
Collapse
Affiliation(s)
- Ronen Taub
- Department of Physiology, Biophysics
& Systems Biology, Medicine Faculty, Technion IIT, Haifa 3525422, Israel
| | - Yonatan Savir
- Department of Physiology, Biophysics
& Systems Biology, Medicine Faculty, Technion IIT, Haifa 3525422, Israel
| |
Collapse
|
9
|
Yang Z, Huang T, Pan L, Wang J, Wang L, Ding J, Xiao J. QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning. J Cheminform 2024; 16:48. [PMID: 38685101 PMCID: PMC11059686 DOI: 10.1186/s13321-024-00843-y] [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: 02/06/2024] [Accepted: 04/24/2024] [Indexed: 05/02/2024] Open
Abstract
Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .
Collapse
Affiliation(s)
- Zhijiang Yang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Tengxin Huang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Li Pan
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Jingjing Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| | - Junhua Xiao
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| |
Collapse
|
10
|
Kengkanna A, Ohue M. Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX. Commun Chem 2024; 7:74. [PMID: 38580841 PMCID: PMC10997661 DOI: 10.1038/s42004-024-01155-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/18/2024] [Indexed: 04/07/2024] Open
Abstract
Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.
Collapse
Affiliation(s)
- Apakorn Kengkanna
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Kanagawa, 226-8501, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Kanagawa, 226-8501, Japan.
| |
Collapse
|
11
|
Guo W, Dong Y, Hao GF. Transfer learning empowers accurate pharmacokinetics prediction of small samples. Drug Discov Today 2024; 29:103946. [PMID: 38460571 DOI: 10.1016/j.drudis.2024.103946] [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: 01/08/2024] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. Recently, transfer learning methods showed promise in predicting ADME/PK parameters. Given the prolific growth of research on transfer learning for PK prediction, a comprehensive review of its advantages and challenges is imperative. This study explores the fundamentals, classifications, toolkits and applications of various transfer learning techniques for PK prediction, demonstrating their utility through three practical case studies. This work will serve as a reference for drug design researchers.
Collapse
Affiliation(s)
- Wenbo Guo
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Yawen Dong
- School of Pharmaceutical Sciences, Guizhou University, Guiyang 550025, China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China.
| |
Collapse
|
12
|
Chen J, Zhu L, Wang J. Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:199-218. [PMID: 38372083 DOI: 10.1080/1062936x.2024.2312527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/25/2024] [Indexed: 02/20/2024]
Abstract
The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.
Collapse
Affiliation(s)
- J Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - L Zhu
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - J Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| |
Collapse
|
13
|
Shi W, Lin K, Zhao Y, Li Z, Zhou T. Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168927. [PMID: 38042202 DOI: 10.1016/j.scitotenv.2023.168927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/17/2023] [Accepted: 11/25/2023] [Indexed: 12/04/2023]
Abstract
The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these compounds, which can be achieved through the deep learning approach. Graph neural networks (GNN) known for its' extraordinary ability to process graph data with rich relationships, have been employed in various molecular prediction tasks. In this study, alicyclic molecules screened from PCBA, Toxcast and Tox21 are made as general bioactivity and biological targets' activity prediction datasets. GNN-based models are trained on the two datasets, while the Attentive FP and PAGTN achieve best performance individually. In addition, alicyclic carbon atoms make the greatest contribution to biological activity, which indicate that the alicycle structures have significant impact on the carbon atoms' contribution. Moreover, there are terrific number of active molecules in other public datasets, indicates that alicyclic compounds deserve more attention in POPs control. This study uncovered deeper structural-activity relationships within these compounds, offering new perspectives and methodologies for academic research in the field.
Collapse
Affiliation(s)
- Wenjie Shi
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China.
| | - Kunsen Lin
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China.
| | - Youcai Zhao
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, 1515 North Zhongshan Rd. (No. 2), Shanghai 200092, PR China
| | - Zongsheng Li
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Tao Zhou
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, 1515 North Zhongshan Rd. (No. 2), Shanghai 200092, PR China.
| |
Collapse
|
14
|
Gao J, Wang Z, Han Y, Gao M, Li J. CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2305918. [PMID: 37702143 DOI: 10.1002/smll.202305918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/31/2023] [Indexed: 09/14/2023]
Abstract
The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (mE ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected mE is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n-type and p-type semiconductors with low mE . Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the mE of any structure, with the area under the curve of 0.904 for n-type semiconductors and 0.896 for p-type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest mE database is built that contains 126 335 entries and screens out 466 semiconductors with low mE for transparent conductive materials, photovoltaic materials, and water-splitting materials. Moreover, a user-friendly and interactive CEEM web is provided that supports query, prediction, and explanation of mE . CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high-performance semiconductors.
Collapse
Affiliation(s)
- Jing Gao
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhilong Wang
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanqiang Han
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Mingyu Gao
- The Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Jinjin Li
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| |
Collapse
|
15
|
Wu Z, Chen J, Li Y, Deng Y, Zhao H, Hsieh CY, Hou T. From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery. J Chem Inf Model 2023; 63:7617-7627. [PMID: 38079566 DOI: 10.1021/acs.jcim.3c01642] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has garnered growing interest for its potential to justify the prediction of black-box machine learning models and provide actionable insights. We first survey a range of XAI techniques adapted for chemical applications and categorize them based on the technical details of each methodology. We then present a few case studies to illustrate the practical utility of XAI, such as identifying carcinogenic molecules and guiding molecular optimizations, in order to provide chemists with concrete examples of ways to take full advantage of XAI-augmented machine learning for chemistry. Despite the initial success of XAI in chemistry, we still face the challenges of developing more reliable explanations, assuring robustness against adversarial actions, and customizing the explanation for different applications and needs of the diverse scientific community. Finally, we discuss the emerging role of large language models like GPT in generating natural language explanations and discusses the specific challenges associated with them. We advocate that addressing the aforementioned challenges and actively embracing new techniques may contribute to establishing machine learning as an indispensable technique for chemistry in this digital era.
Collapse
Affiliation(s)
- Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
- CarbonSilicon AI Technology Company, Limited, Hangzhou, 310018 Zhejiang, P. R. China
| | - Jihong Chen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
- CarbonSilicon AI Technology Company, Limited, Hangzhou, 310018 Zhejiang, P. R. China
| | - Yitong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
| | - Yafeng Deng
- CarbonSilicon AI Technology Company, Limited, Hangzhou, 310018 Zhejiang, P. R. China
| | - Haitao Zhao
- Center for Intelligent and Biomimetic Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 440305 Guangdong, P. R. China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
| |
Collapse
|
16
|
Zhang Y, Liu C, Liu M, Liu T, Lin H, Huang CB, Ning L. Attention is all you need: utilizing attention in AI-enabled drug discovery. Brief Bioinform 2023; 25:bbad467. [PMID: 38189543 PMCID: PMC10772984 DOI: 10.1093/bib/bbad467] [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: 09/30/2023] [Revised: 11/03/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024] Open
Abstract
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
Collapse
Affiliation(s)
- Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Caiqi Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
- Key Laboratory of Molecular Oncology of Heilongjiang Province, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
| | - Mujiexin Liu
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tianyuan Liu
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Lin Ning
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
| |
Collapse
|
17
|
Xu F, Yang Z, Wang L, Meng D, Long J. MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction. Brief Bioinform 2023; 25:bbad423. [PMID: 38048081 PMCID: PMC10753536 DOI: 10.1093/bib/bbad423] [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: 06/15/2023] [Revised: 09/18/2023] [Accepted: 10/29/2023] [Indexed: 12/05/2023] Open
Abstract
Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out node information or lose the connection of the original graph; therefore, it is difficult to identify continuous subtructures. Importantly, they lacked interpretability on molecular graphs. To this end, we proposed a novel Molecular Edge Shrinkage Pooling (MESPool) method, which is based on edges (or chemical bonds). MESPool preserves crucial edges and shrinks others inside the functional groups and is able to search for key structures without breaking the original connection. We compared MESPool with various well-known pooling methods on different benchmarks and showed that MESPool outperforms the previous methods. Furthermore, we explained the rationality of MESPool on some datasets, including a COVID-19 drug dataset.
Collapse
Affiliation(s)
- Fanding Xu
- School of Life Science and Technology, Xi’an Jiaotong University, 710049 Shaanxi, China
| | - Zhiwei Yang
- School of Physics, Xi’an Jiaotong University, 710049 Shaanxi, China
| | - Lizhuo Wang
- School of Life Science and Technology, Xi’an Jiaotong University, 710049 Shaanxi, China
| | - Deyu Meng
- Rearch Institute for Mathematics and Mathematical Technology, Xi’an Jiaotong University, 710049 Shaanxi, China
- School of Mathematics and Statistics, Henan University, 475004 Henan, China
| | - Jiangang Long
- School of Life Science and Technology, Xi’an Jiaotong University, 710049 Shaanxi, China
| |
Collapse
|
18
|
Fan F, Wu G, Yang Y, Liu F, Qian Y, Yu Q, Ren H, Geng J. A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18236-18245. [PMID: 37749748 DOI: 10.1021/acs.est.3c04571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model's prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models.
Collapse
Affiliation(s)
- Fan Fan
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
| | - Gang Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
| | - Yining Yang
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Fu Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
| | - Yuli Qian
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
| | - Qingmiao Yu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
| | - Jinju Geng
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China
- Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University, Chongqing 400044, China
| |
Collapse
|
19
|
Zhu W, Wang Y, Niu Y, Zhang L, Liu Z. Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable? HEALTH DATA SCIENCE 2023; 3:0098. [PMID: 38487200 PMCID: PMC10880170 DOI: 10.34133/hds.0098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/20/2023] [Indexed: 03/17/2024]
Abstract
Importance: Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights: In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion: Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.
Collapse
Affiliation(s)
- Wenyu Zhu
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Yanxing Wang
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Yan Niu
- Department of Medicinal Chemistry,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs,
School of Pharmaceutical Sciences, Peking University, 100191 Beijing, P. R. China
| |
Collapse
|
20
|
Yu J, Li Z, Chen G, Kong X, Hu J, Wang D, Cao D, Li Y, Huo R, Wang G, Liu X, Jiang H, Li X, Luo X, Zheng M. Computing the relative binding affinity of ligands based on a pairwise binding comparison network. NATURE COMPUTATIONAL SCIENCE 2023; 3:860-872. [PMID: 38177766 PMCID: PMC10766524 DOI: 10.1038/s43588-023-00529-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/05/2023] [Indexed: 01/06/2024]
Abstract
Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.
Collapse
Affiliation(s)
- Jie Yu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Information Science and Technology, Shanghai Tech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Zhaojun Li
- College of Computer and Information Engineering, Dezhou University, Dezhou City, China
- Development Department, Suzhou Alphama Biotechnology Co., Ltd, Suzhou City, China
| | - Geng Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xiangtai Kong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jie Hu
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Duanhua Cao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yanbei Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Ruifeng Huo
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Gang Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaohong Liu
- Development Department, Suzhou Alphama Biotechnology Co., Ltd, Suzhou City, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
- State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, Jiangsu, China.
| |
Collapse
|