Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Institute of Open Source Software and Platform, Peng Cheng Laboratory（鹏城实验室开源软件与平台研究所）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University（湖南大学化学生物传感与计量学国家重点实验室）
Self-supervised representation learning (SSL) on biomedical networks provides new opportunities for drug discovery. However, how to effectively combine multiple SSL models is still challenging and has been rarely explored. Therefore, we propose multi-task joint strategies of self-supervised representation learning on biomedical networks for drug discovery, named MSSL2drug. We design six basic SSL tasks inspired by various modality features including structures, semantics, and attributes in heterogeneous biomedical networks. Importantly, fifteen combinations of multiple tasks are evaluated by a graph attention-based multi-task adversarial learning framework in two drug discovery scenarios. The results suggest two important findings. (1) Combinations of multimodal tasks achieve the best performance compared to other multi-task joint models. (2) The local-global combination models yield higher performance than random two-task combinations when there are the same size of modalities. Therefore, we conjecture that the multimodal and local-global combination strategies can be treated as the guideline of multi-task SSL for drug discovery.
The concern of overconfident mis-predictions under distributional shift demands extensive reliability research on Graph Neural Networks used in critical tasks in drug discovery. Here we first introduce CardioTox, a real-world benchmark on drug cardio-toxicity to facilitate such efforts. Our exploratory study shows overconfident mis-predictions are often distant from training data. That leads us to develop distance-aware GNNs: GNN-SNGP. Through evaluation on CardioTox and three established benchmarks, we demonstrate GNN-SNGP's effectiveness in increasing distance-awareness, reducing overconfide…
Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations
Data Science Institute, School of Mathematics, Shandong University（数据科学研究院、数学学院、山东大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学与系统科学研究院、中国科学院）；Data Science Institute, Shandong University（数据科学研究院、山东大学）
PepGB: Facilitating peptide drug discovery via graph neural networks
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Machine Learning Department, Silexon AI Technology Co., Ltd.（赛莱克森人工智能科技有限公司机器学习部）；School of Engineering, Westlake University（西湖大学工学院）；School of Pharmacy, Second Military Medical University（海军军医大学药学院）
A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；School of Computing, Mathematics & Engineering, Charles Sturt University（查尔斯斯特大学计算、数学与工程学院）；Artificial Intelligence & Cyber Futures Institute, Charles Sturt University（查尔斯斯特大学人工智能与网络未来研究院）；Faculty of Engineering, University of Mines and Technology（矿业技术大学工程学院）；School of Mathematics and Computer Science, Xinyu University（新余学院数学与计算机学院）
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Edwardson School of Industrial Engineering and the Institute of Cancer Research, Purdue University（埃德沃兹工业工程学院和癌症研究所，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家庭电气与计算机工程学院，普渡大学）
The University of Texas at Austin（得克萨斯大学奥斯汀分校）；AbbVie Inc.（艾伯维公司）；Indiana University（印第安纳大学）
Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven tasks such as drug discovery rely on incorporating information from different biomedical databases. These databases contain various biological entities and relations such as proteins (PDB), genes (Gene Ontology), drugs (DrugBank), diseases (DDB), and protein-protein interactions (BioGRID). The process of semantically integrating heterogeneous biomedical databases is often riddled w…
MUDI: A Multimodal Biomedical Dataset for Understanding Pharmacodynamic Drug-Drug Interactions
VNU University of Engineering and Technology (VNU-UET)（越南工程技术大学）；Lausanne University Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；Hanoi Medical University（河内医学院）；National Geriatric Hospital, Hanoi（河内国立老年医院）
DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment
Department of Computer Science and Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Computational Biology Branch, National Library of Medicine（国家医学图书馆计算生物学分支）；Khoury College of Computer Sciences, Northeastern University（东北大学计算机科学学院）；State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University（南京大学新型软件技术国家重点实验室，南京大学计算机科学学院）；Developmental Therapeutics Branch, National Cancer Institute（国家癌症研究所发育治疗分支）
Monitoring the biomedical literature for cases of Adverse Drug Reactions (ADRs) is a critically important and time consuming task in pharmacovigilance. The development of computer assisted approaches to aid this process in different forms has been the subject of many recent works. One particular area that has shown promise is the use of Deep Neural Networks, in particular, Convolutional Neural Networks (CNNs), for the detection of ADR relevant sentences. Using token-level convolutions and general purpose word embeddings, this architecture has shown good performance relative to more traditional…
Boosting drug-disease association prediction for drug repositioning via dual-feature extraction and cross-dual-domain decoding
Institute of Computing Science and Technology, Guangzhou University（广州大学计算科学与技术学院）；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）；Electronics and Communication Sciences Unit, Indian Statistical Institute（印度统计研究所电子与通信科学部）
KITE-DDI: A Knowledge graph Integrated Transformer Model for accurately predicting Drug-Drug Interaction Events from Drug SMILES and Biomedical Knowledge Graph
Siemens AG（西门子股份公司）；Ludwig Maximilian University of Munich（慕尼黑大学）
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approac…
Rochester Institute of Technology（罗切斯特理工学院）；Thomas H. Gosnell School of Life Sciences（托马斯·H·戈斯内尔生命科学学院）
Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the-art results in biomedical interaction prediction. These methods only consider information from immediate neighbors but cannot learn a general mixing of features from neighbors at various distances. In this paper, we present a higher-order …
Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding
Computer Science, University of Waterloo, Canada（计算机科学，滑铁卢大学，加拿大）；Applied Math, University of Waterloo, Canada（应用数学，滑铁卢大学，加拿大）；Public Health Sciences, University of Waterloo, Canada（公共卫生科学，滑铁卢大学，加拿大）
Tencent AI Lab（腾讯人工智能实验室）；The University of Hong Kong（香港大学）；Fudan University（复旦大学）
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its extensive use in many fields, such as ADMET prediction, virtual screening, protein folding and generative chemistry, little has been explored in terms of the out-of-distribution (OOD) learning problem with \emph{noise}, which is inevitable in real world AIDD applications. In this work, we present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery, which comes with an open-source Pyth…
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e., molecular property prediction and molecule generation. We then discuss common data resources, molecule representations and benchmark platforms. Furthermore, to summarize the progress of AI in drug discovery, we present the relevant AI technique…
Global Health Drug Discovery Institute（全球健康药物研发中心）
The study of molecule-target interaction is quite important for drug discovery in terms of target identification, hit identification, pathway study, drug-drug interaction, etc. Most existing methodologies utilize either biomedical network information or molecule structural features to predict potential interaction link. However, the biomedical network information based methods usually suffer from cold start problem, while structure based methods often give limited performance due to the structure/interaction assumption and data quality. To address these issues, we propose a pseudo-siamese Grap…
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；College of Computer Science, Xiangtan University（湘潭大学计算机学院）；Tencent AI Lab（腾讯AI实验室）；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机系）
DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
Yale University（耶鲁大学）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）；The Chinese University of Hong Kong（香港中文大学）；University of California, Berkeley（加州大学伯克利分校）；UC Berkeley（加州大学伯克利分校）
Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis. Computational approaches are widely applied to drug development because of the increasing availability of biological molecular datasets. Popular generative approaches can create new drug molecules by learning the given molecule distributions. However, these approaches are mostly not for target-specific drug discovery. We developed an energy-based probabilistic model for computational target-specific drug discovery. Results show that our proposed TagMol can generate molecules with similar binding af…
University of Washington（华盛顿大学）；Allen Institute for Artificial Intelligence (AI2)（艾伦人工智能研究所）
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applications, such as drug design and repurposing. Recent work has shown that general-domain language models (LMs) can serve as "soft" KGs, and that they can be fine-tuned for the task of KG completion. In this work, we study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We evaluate several domain-specific LMs, fine-tuning them on datasets cent…
Peking University（北京大学）；School of Pharmaceutical Sciences, Peking University（北京大学药学院）
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the …
University of California, Berkeley（加州大学伯克利分校）；University of Chicago（芝加哥大学）；University of Notre Dame（圣母大学）
In the past several months, COVID-19 has spread over the globe and caused severe damage to the people and the society. In the context of this severe situation, an effective drug discovery method to generate potential drugs is extremely meaningful. In this paper, we provide a methodology of discovering potential drugs for the treatment of Severe Acute Respiratory Syndrome Corona-Virus 2 (commonly known as SARS-CoV-2). We proposed a new model called Genetic Constrained Graph Variational Autoencoder (GCGVAE) to solve this problem. We trained our model based on the data of various viruses' protein…
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, …
The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. Th…
Relation Therapeutics（Relay Therapeutics（注：原文为Relation Therapeutics，直译：关系治疗公司））；University of Cambridge（剑桥大学）；Juvenescence（Juvenescence公司）；Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；HEC Montreal（蒙特利尔高等商学院）；The Francis Crick Institute（弗朗西斯·克里克研究所）；Imperial College London（帝国理工学院）；Twitter（推特）
Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of sma…
Tsinghua University（清华大学）；Ping An Technology（平安科技）；Beijing University of Chemical Technology（北京化工大学）；Lanzhou University（兰州大学）
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and have poor generalization capability. Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful MolGNet model and an effective self-supervised strategy for pre-tra…
European Molecular Biology Laboratory（欧洲分子生物学实验室）；European Bioinformatics Institute（欧洲生物信息研究所）；University of Cambridge（剑桥大学）
Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings. Among the large number of algorithms developed over the last decades to compute prediction errors, Conformal Prediction (CP) has gained increasing attention in the computational drug discovery community. A major reason for its recent popularity is the ease of interpretation of the computed prediction errors in both classification and regression t…
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the following challenge: can a nontrivial AI model reproduce natural chemical diversity for desired molecules? To illustrate this question, we consider two generative models: a Reinforcement Learning model and the recently introduced ORGAN. Both fail at this challenge. We hope this c…
Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for 'explainable' deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This review summarizes the most prominent algorithmic concepts of explainable …
Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open-access antiviral drug resource and machine learning platform (VDDB), which, to the best of our knowledge, is the first comprehensive dedicated resource for experimentally verified potential drugs/molecules based on manually curated data. Currently, VDDB highlights 848 clinical vaccines, 199 clinical antibodies, as well as over 710,000 …
Eindhoven University of Technology（埃因霍温理工大学）；Microsoft Research Cambridge（微软剑桥研究院）；Centre for Living Technologies（生命技术中心）
Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in dee…
Chalmers University of Technology（查尔姆斯理工大学）；AstraZeneca AB（阿斯利康公司）
We study the problem of performing automated experiment design for drug screening through Bayesian inference and optimisation. In particular, we compare and contrast the behaviour of linear-Gaussian models and Gaussian processes, when used in conjunction with upper confidence bound algorithms, Thompson sampling, or bounded horizon tree search. We show that non-myopic sophisticated exploration techniques using sparse tree search have a distinct advantage over methods such as Thompson sampling or upper confidence bounds in this setting. We demonstrate the significant superiority of the approach …
Georgia Institute of Technology（佐治亚理工学院）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；IQVIA（艾昆纬）
Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g., experimental data). Most of the existing approaches ignore KGs altogether. Some try to directly integrate KGs with other…
The Ohio State University（俄亥俄州立大学）；The Research Institute at Nationwide Children’s Hospital（全国儿童医院研究所）；Huazhong Agricultural University（华中农业大学）
Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evalua…
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials. Over past decades, drug discovery and development have been a costly and time consuming process. Driven by big chemical data and AI, deep generative models show great potential to accelerate the drug discovery process. Existing works investigate different deep generative frameworks for molecular generation, however, less attention has been paid to the visualization tools to quickly demo and evaluate model's results. Here, we propose a visualization framework which provides interactive visuali…
