Modern cancer -omics and pharmacological data hold great promise in precision cancer medicine for developing individualized patient treatments. However, high heterogeneity and noise in such data pose challenges for predicting the response of cancer cell lines to therapeutic drugs accurately. As a result, arbitrary human judgment calls are rampant throughout the predictive modeling pipeline. In this work, we develop a transparent stability-driven pipeline for drug response interpretable predictions, or staDRIP, which builds upon the PCS framework for veridical data science (Yu and Kumbier, 2020) and mitigates the impact of human judgment calls. Here we use the PCS framework for the first time in cancer research to extract proteins and genes that are important in predicting the drug responses and stable across appropriate data and model perturbations. Out of the 24 most stable proteins we identified using data from the Cancer Cell Line Encyclopedia (CCLE), 18 have been associated with the drug response or identified as a known or possible drug target in previous literature, demonstrating the utility of our stability-driven pipeline for knowledge discovery in cancer drug response prediction modeling.
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
We investigate molecular mechanisms of resistant or sensitive response of cancer drug combination therapies in an inductive and interpretable manner. Though deep learning algorithms are widely used in the drug synergy prediction problem, it is still an open problem to formulate the prediction model with biological meaning to investigate the mysterious mechanisms of synergy (MoS) for the human-AI collaboration in healthcare systems. To address the challenges, we propose a deep graph neural network, IDSP (Interpretable Deep Signaling Pathways), to incorporate the gene-gene as well as gene-drug r…
DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model
Regenerative Medicine Research Center, Isfahan University of Medical Sciences（再生医学研究中心，伊斯法罕医科大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences（伊斯法罕神经科学研究中心，伊斯法罕医科大学）；Department of Electrical and Computer Engineering, Isfahan University of Technology（电气与计算机工程系，伊斯法罕技术大学）
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz（林茨约翰开普勒大学LIT人工智能实验室和机器学习研究所）；Computational Chemistry & Biology, Merck KGaA（默克集团计算化学与生物学部）
Without any means of interpretation, neural networks that predict molecular properties and bioactivities are merely black boxes. We will unravel these black boxes and will demonstrate approaches to understand the learned representations which are hidden inside these models. We show how single neurons can be interpreted as classifiers which determine the presence or absence of pharmacophore- or toxicophore-like structures, thereby generating new insights and relevant knowledge for chemistry, pharmacology and biochemistry. We further discuss how these novel pharmacophores/toxicophores can be det…
A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model
School of Medicine, National Tsing Hua University（国立清华大学医学院）；Institute of Bioinformatics and Structural Biology, National Tsing Hua University（国立清华大学生物信息学与结构生物学研究所）；Department of Life Science, National Tsing Hua University（国立清华大学生命科学系）；Interdisciplinary Program of Life Sciences and Medicine, National Tsing Hua University（国立清华大学生命科学与医学跨学科计划）
Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have tw…
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）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (1) the sole data-driven molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain; (2) existing methods focus on limited labeled data while ignoring the value of massi…
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Computer Science and Technology, Nanjing Tech University（南京工业大学计算机科学与技术学院）
Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations. In this paper, we proposed a novel method, Graph2MDA, to predict microbe-…
Harvard T. H. Chan School of Public Health, Harvard University（哈佛大学陈曾熙公共卫生学院）；IQVIA（艾昆纬）；MIT-IBM Watson AI Lab, IBM Research（IBM研究院麻省理工学院-IBM沃森人工智能实验室）；Georgia Institute of Technology（佐治亚理工学院）
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and society. Although several computational models have been proposed for DDI prediction, there are still limitations: (1) specialized design of drug representation for DDI predictions is lacking; (2) predictions are based on limited labelled data and do not generalize well to unseen drugs or DDIs; and (3) models are characterized by a large number of parameters, thus are hard to interpret. In this work, we develop a ChemicA…
MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms
School of Medicine, National Tsing Hua University（国立清华大学医学院）；Institute of Bioinformatics and Structural Biology, National Tsing Hua University（国立清华大学生物信息与结构生物学研究所）；Department of Life Science, National Tsing Hua University（国立清华大学生命科学系）
Interpretable Causal Representation Learning for Biological Data in the Pathway Space
CIMA University of Navarra, CCUN, IdiSNA（纳瓦拉大学CIMA、CCUN、IdiSNA）；TECNUN, University of Navarra, San Sebastián（纳瓦拉大学TECNUN、桑塞巴斯蒂安）；Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST)（科学技术大学（KAUST）生物与环境科学与工程部门）；Dept. of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic（梅奥诊所分子药理学与实验治疗学部门）；Institute of Chemical Biology, Ilia State University（伊利亚州立大学化学生物学研究所）；Center for Data Science (DATAI), University of Navarra（纳瓦拉大学数据科学中心（DATAI））
Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations
Interdisciplinary Program in Bioinformatics, Seoul National University（首尔国立大学生物信息学跨学科项目）；AIGENDRUG Co., Ltd.（爱真药物有限公司）；BK21 FOUR Intelligence Computing, Seoul National University（首尔国立大学BK21四号智能计算）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Department of Artificial Intelligence, Inha University（仁荷大学人工智能系）
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…
IBM Research Zurich（IBM苏黎世研究院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；RWTH Aachen University（亚琛工业大学）；Heidelberg University（海德堡大学）
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based (MCA) encoder significantly outperforms a baseline mod…
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives
Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China（山西大学大数据科学与产业研究院）；School of Computer and Information Technology, Shanxi University, Taiyuan, China（山西大学计算机与信息学院）；Key Laboratory of Evolutionary Science Intelligence of Shanxi Province, Taiyuan, Shanxi, China（山西省进化科学智能重点实验室）；Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran（伊斯法罕大学计算机工程学院）；The First Clinical Medical School, Shanxi Medical University, Taiyuan, China（山西医科大学第一临床医学院）；School of Medicine & Population Health, University of Sheffield, Sheffield, UK（谢菲尔德大学医学院与人口健康学院）；School of Computer Science, University of Sheffield, Sheffield, UK（谢菲尔德大学计算机科学学院）；Centre for Machine Intelligence, University of Sheffield, Sheffield, UK（谢菲尔德大学智能中心）
Uncovering Population PK Covariates from VAE-Generated Latent Spaces
Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova（帕多瓦大学心脏、胸腔、血管科学与公共卫生系）；School of Engineering, London South Bank University（伦敦南岸大学工程学院）
