Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. For decision makings in virtual screening, researchers find it useful to interpret an output of classification system as probability, since such interpretation allows them to filter out more desirable compounds. However, probabilistic interpretation cannot be correct for models that hold over-parameterization problems or inappropriate regularizations, leading to unreliable prediction and decision making. In this regard, we concern the reliability of neural prediction models on molecular properties, especially when models are trained with sparse data points and imbalanced distributions. This work aims to propose guidelines for training reliable models, we thus provide methodological details and ablation studies on the following train principles. We investigate the effects of model architectures, regularization methods, and loss functions on the prediction performance and reliability of classification results. Moreover, we evaluate prediction reliability of models on virtual screening scenario. Our result highlights that correct choice of regularization and inference methods is evidently important to achieve high success rate, especially in data imbalanced situation. All experiments were performed under a single unified model implementation to alleviate external randomness in model training and to enable precise comparison of results.
Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpreted as predictive probability will be beneficial, in that a high prediction score corresponds to high probability of correctness. In this work, we present a study on the prediction performance and reliability of graph neural networks trained with the recently proposed Bayesian learning algorithms. Our work shows that Bayesian learning algorithms allow well-calibrated predictions for various GNN architectures and class…
Global Health Drug Discovery Institute（全球健康药物研发中心）
Structure-based virtual screening (SBVS) is a promising in silico technique that integrates computational methods into drug design. An extensively used method in SBVS is molecular docking. However, the docking process can hardly be computationally efficient and accurate simultaneously because classic mechanics scoring function is used to approximate, but hardly reach, the quantum mechanics precision in this method. In order to reduce the computational cost of the protein-ligand scoring process and use data driven approach to boost the scoring function accuracy, we introduce a docking-based SBV…
University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）
We present a new method for understanding the performance of a model in virtual drug screening tasks. While most virtual screening problems present as a mix between ranking and classification, the models are typically trained as regression models presenting a problem requiring either a choice of a cutoff or ranking measure. Our method, regression enrichment surfaces (RES), is based on the goal of virtual screening: to detect as many of the top-performing treatments as possible. We outline history of virtual screening performance measures and the idea behind RES. We offer a python package and d…
Institute for Neurodegenerative Diseases（神经退行性疾病研究所）；University of California, San Francisco（加州大学旧金山分校）
The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches. We describe an end-to-end deep learning approach that operates directly on small-molecule conformational ensembles and identifies key conformational poses of small-molecules. Our networks leverage two levels of representation learning: 1) individual conformers are first encoded as spatial graphs using a graph neural network, and 2) sampled conformational ensembles are represented as sets using an a…
Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening
Technology and Operations Management Area, INSEAD, Fontainebleau, France（INSEAD技术与运营管理部）；Advanced Institute of Business and School of Economics and Management, Tongji University, Shanghai, China（同济大学高级商学院）；Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota（明尼苏达大学工业与系统工程系）
Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of $10^8$ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques can aid in their exploration: a surrogate structure-property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising …
Brookhaven National Laboratory（布鲁克海文国家实验室）；Texas A&M University（德克萨斯农工大学）
The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the large size of the search space containing the candidates and the substantial computational cost of high-fidelity property prediction models makes screening practically challenging. In this work, we propose a general framework for constructing and optimizing a virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allo…
Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the knowledge of the other targets and take advantage of the existing data, in this work, we apply multi-task learning to the problem of docking-based virtual screening. With two large docking datasets, the results of extensive experiments show that multi-task learning can achieve better performances on docking score prediction. By learning knowledge across multip…
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
Institute for AI Industry Research（人工智能产业研究院）；Tsinghua University（清华大学）；Peking University（北京大学）；Institute of Automation（自动化研究所）；Chinese Academy of Science（中国科学院）
Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Dana Farber Cancer Institute（达纳-法伯癌症研究所）
Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction …
Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data
Zhejiang University of Technology（浙江工业大学）；Macao Polytechnic University（澳门理工大学）
ELLIS Unit at the LIT AI Lab, Johannes Kepler University Linz（林茨约翰内斯·开普勒大学 LIT AI 实验室 ELLIS 单元）；Institute for Machine Learning, Johannes Kepler University Linz（林茨约翰内斯·开普勒大学机器学习研究所）
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are …
Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Petuum Inc.（Petuum公司）
Harvard University（哈佛大学）；MIT（麻省理工学院）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；IBM Research Europe（IBM欧洲研究院）
High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model-guided optimization has been employed to lower these costs through dramatic increases in sample efficiency compared to random selection. However, these techniques introduce new costs to the workflow through the surrogate model training and inference steps. In this study, we propose an extension to the framework of model-guided optimization that mitigates infe…
Leipzig University（莱比锡大学）；Technical University of Munich（慕尼黑工业大学）；Vanderbilt University（范德堡大学）
Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. In this work we argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance, its abil…
Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching
Department of Computer Science, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校计算机科学系）；Eshelman School of Pharmacy, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校埃舍尔曼药学院）
Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Innovation and Entrepreneurship, Dalian University of Technology（大连理工大学创新创业学院）
Fitness aligned structural modeling enables scalable virtual screening with AuroBind
Shanghai Jiao Tong University（上海交通大学）；Lingang Laboratory（临港实验室）；Sun Yat-sen University（中山大学）；Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Chinese Academy of Sciences（中国科学院）；Massachusetts Institute of Technology（麻省理工学院）；Ningxia Medical University（宁夏医科大学）
Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）
Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceed…
Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening
School of Computer Science, University of Nottingham Ningbo（诺丁汉大学宁波分校计算机学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；Center for Frontier AI Research, Institute of High Performance Computing, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院高性能计算研究所前沿人工智能研究中心（新加坡科技研究局））；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
In silico prediction of cardiotoxicity with high sensitivity and specificity for potential drug molecules can be of immense value. Hence, building machine learning classification models, based on some features extracted from the molecular structure of drugs, which are capable of efficiently predicting cardiotoxicity is critical. In this paper, we consider the application of various machine learning approaches, and then propose an ensemble classifier for the prediction of molecular activity on a Drug Discovery Hackathon (DDH) (1st reference) dataset. We have used only 2-D descriptors of SMILE n…
Learning-to-rank, a machine learning technique widely used in information retrieval, has recently been applied to the problem of ligand-based virtual screening, to accelerate the early stages of new drug development. Ranking prediction models learn based on ordinal relationships, making them suitable for integrating assay data from various environments. Existing studies of rank prediction in compound screening have generally used a learning-to-rank method called RankSVM. However, they have not been compared with or validated against the gradient boosting decision tree (GBDT)-based learning-to-…
Jagiellonian University（雅盖隆大学）；Maj Institute of Pharmacology, Polish Academy of Sciences（波兰科学院马伊药理学研究所）
While there is a general focus on predictions of values, mathematically more appropriate is prediction of probability distributions: with additional possibilities like prediction of uncertainty, higher moments and quantiles. For the purpose of the computer-aided drug design field, this article applies Hierarchical Correlation Reconstruction approach, previously applied in the analysis of demographic, financial and astronomical data. Instead of a single linear regression to predict values, it uses multiple linear regressions to independently predict multiple moments, finally combining them into…
A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It
School of Science and Technology, Champions College Prep（科学与技术学院，冠军大学预科）
Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能跨学科项目，首尔国立大学）；Department of Computer Science and Engineering, Seoul National University（计算机科学与工程系，首尔国立大学）；Interdisciplinary Program in Bioinformatics, Seoul National University（生物信息学跨学科项目，首尔国立大学）；AIGENDRUG Co., Ltd., Seoul（AIGENDRUG公司，首尔）；Department of Artificial Intelligence, Inha University（人工智能系，inha大学）
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Sandia National Laboratories（桑迪亚国家实验室）；NVIDIA Corporation（英伟达公司）
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coh…
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Institute for AI Industry Research, Tsinghua University, Beijing, China（人工智能产业研究院，清华大学，北京，中国）；Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China（北京生物结构前沿研究中心，清华大学，北京，中国）；Beijing Academy of Artificial Intelligence (BAAI), Beijing, China（北京人工智能研究院（BAAI），北京，中国）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（计算机科学与技术系，清华大学，北京，中国）；University of Electronic Science and Technology of China, Chengdu, China（电子科技大学，成都，中国）
Combining GCN Structural Learning with LLM Chemical Knowledge for Enhanced Virtual Screening
Department of Computer Science , University USTHB（计算机科学系，USTHB大学）；Intelligent Systems Enginnering, National School of Artificial Intelligence (ENSIA)（智能系统工程，国家人工智能学校（ENSIA））；Laboratory of Physical Chemistry and Biological Materials（物理化学与生物材料实验室）
Argonne National Laboratory（阿贡国家实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）
We propose a benchmark to study surrogate model accuracy for protein-ligand docking. We share a dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. Our work shows surrogate docking models have six orders of magnitude more throughput than standard docking protocols on the same supercomputer node types. We demonstrate the power of high-speed surrogate models by running each target against 1 billion molecules in under a day (50k predictions pe…
Scaffold-Aware Generative Augmentation and Reranking for Enhanced Virtual Screening
Yale University（耶鲁大学）；University of Oregon（俄勒冈大学）；Broad Institute of MIT and Harvard（MIT和哈佛大学Broad研究所）；Leipzig University（莱比锡大学）；Vanderbilt University（范德比大学）
Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering（化学与生物传感国家重点实验室，计算机科学与电子工程学院；教育部长江超级计算与人工智能融合计算重点实验室）；The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence（湖南大学）；Hunan University（人工智能与计算机科学学院）；School of Artificial Intelligence and Computer Science（江南大学）；Jiangnan University（计算机科学系）；Department of Computer Science（筑波大学）；University of Tsukuba（中医学学院）；School of Chinese Medicine（香港 Baptist 大学）；Hong Kong Baptist University（信息学院，国家健康与医学数据科学研究院）；School of Informatics, National Institute for Data Science in Health and Medicine
