By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions. In this paper we train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features. We found the inclusion of single nucleotide polymorphisms (SNPs) coded as count matrices improved model performance significantly, and no substantial difference in model performance with respect to molecular featurization between the common open source MOrdred descriptors and Dragon7 descriptors. Alongside this analysis, we outline data integration between CCL screening datasets and present evidence that new metrics and imbalanced data techniques, as well as advances in data standardization, need to be developed.
The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) properties of drug candidates are estimated to account for up to 50% of all clinical trial failures. Predicting ADMET properties has therefore been of great interest to the cheminformatics and medicinal chemistry communities in recent decades. Traditional cheminformatics approaches, whether the learner is a random forest or a deep neural network, leverage fixed fingerprint feature representations of molecules. In contrast, in this paper, we learn the features most relevant to each chemical task at hand by representing …
Centrum Wiskunde & Informatica（数学与计算机科学中心）；Utrecht University（乌得勒支大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）
Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning …
Large variability between cell lines brings a difficult optimization problem of drug selection for cancer therapy. Standard approaches use prediction of value for this purpose, corresponding e.g. to expected value of their distribution. This article shows superiority of working on, predicting the entire probability distributions - proposing basic tools for this purpose. We are mostly interested in the best drug in their batch to be tested - proper optimization of their selection for extreme statistics requires knowledge of the entire probability distributions, which for distributions of drug p…
Argonne National Laboratory（阿贡国家实验室）；The University of Chicago（芝加哥大学）
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, n…
Predicting clinical outcomes to anti-cancer drugs on a personalized basis is challenging in cancer treatment due to the heterogeneity of tumors. Traditional computational efforts have been made to model the effect of drug response on individual samples depicted by their molecular profile, yet overfitting occurs because of the high dimension for omics data, hindering models from clinical application. Recent research shows that deep learning is a promising approach to build drug response models by learning alignment patterns between drugs and samples. However, existing studies employed the simpl…
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；GlaxoSmithKline（葛兰素史克）；Frederick National Laboratory（弗雷德里克国家实验室）
One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing machine learning models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools. We have benchmarked AMPL on a large collection of ph…
King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；New Jersey Institute of Technology（新泽西理工学院）
Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measured in human cancer cell lines. Real-world cancer cell lines contain noise, which degrades the performance of machine learning algorithms. This problem is rarely addressed in the existing approaches. In this paper, we present a noise-filtering approach that integrates techniques …
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization
The Ohio State University（俄亥俄州立大学）；Biomedical Informatics Department, The Ohio State University（俄亥俄州立大学生物医学信息学系）；Computer Science and Engineering Department, The Ohio State University（俄亥俄州立大学计算机科学与工程系）；Translational Data Analytics Institute, The Ohio State University（俄亥俄州立大学转化数据分析研究所）
Harbin Institute of Technology at Weihai（哈尔滨工业大学（威海））；University of Edinburgh（爱丁堡大学）；Sun Yat-sen University（中山大学）；University of Arizona（亚利桑那大学）
Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug responses. Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data and encodes these data with our {\sc {GeneVae}} model, which is an ordinary VAE model, and a rectified junction tree variational autoencoder ({\sc JTVae}) model, respectiv…
IBM Research – Zurich（IBM苏黎世研究院）；Institute für Molekulare Systembiologie（分子系统生物学研究所）
We present the Network-based Biased Tree Ensembles (NetBiTE) method for drug sensitivity prediction and drug sensitivity biomarker identification in cancer using a combination of prior knowledge and gene expression data. Our devised method consists of a biased tree ensemble that is built according to a probabilistic bias weight distribution. The bias weight distribution is obtained from the assignment of high weights to the drug targets and propagating the assigned weights over a protein-protein interaction network such as STRING. The propagation of weights, defines neighborhoods of influence …
Washington University School of Medicine, Washington University in St. Louis（圣路易斯华盛顿大学医学院）；Dalian University of Technology（大连理工大学）
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computational models to predict and prioritize effective drug combinations is important for combinatory therapy discovery in cancer. In this study, we proposed a novel deep learning model, AuDNNsynergy, to prediction drug combinations by integrating multi-omics data and chemical structur…
Argonne National Laboratory（阿贡国家实验室）；Leidos Biomedical Research, Inc.（莱多斯生物医学研究公司）；National Cancer Institute（美国国家癌症研究所）；The University of Chicago（芝加哥大学）
Transfer learning has been shown to be effective in many applications in which training data for the target problem are limited but data for a related (source) problem are abundant. In this paper, we apply transfer learning to the prediction of anti-cancer drug response. Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. We apply the classic trans…
Ping An Healthcare Technology（平安医疗科技）；Chinese Academy of Medical Sciences（中国医学科学院）；National Cancer Center（国家癌症中心）；Xidian University School of Computer Science and Technology（西安电子科技大学计算机科学与技术学院）
Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery. CandidateDrug4Cancer dataset encompasses multiple most-mentioned 29 targets for cancer, covering 54869 cancer-related drug molecules which are ranged from pre-clinical, clinical and FDA-approved. Besides building the datasets, we also perform benchmark experiments with effective Drug Target I…
TOBB University of Economics and Technology（TOBB经济与技术大学）；Hacettepe University（哈塞特佩大学）
Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recent advances in producing large drug screens against cancer cell lines provided an opportunity to apply machine learning methods for this purpose. In addition to cytotoxicity databases, considerable amount of drug-induced gene expression data has also become publicly available. Following this, several methods that exploit omics data were proposed to predict drug activity on cancer cells. However, due to the complexity of cancer drug mechanisms, n…
Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心格里希儿童癌症研究所）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Northwestern Polytechnical University（西北工业大学）；Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心流行病学与生物统计学系）
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contain…
SDIVF R&D Centre（SDIVF研发中心）；The Chinese University of Hong Kong（香港中文大学）
Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to test their models and methods. Previously, most research in these areas starts from the fingerprints or features of drugs, instead of their structures. In this paper, we introduce a model for phenotypic screening, which is called twin Convolutional Neural Network for drugs in SMILES format (tCNNS). tCNNS is comprised of CNN input channels for dru…
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；Columbia University（哥伦比亚大学）
Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model for dose-response modeling in multi-sample, multi-treatment cancer drug studies. BTF uses low-dimensional embeddings to share statistical strength between similar drugs and similar cell lines. Structured shrinkage priors in BTF encourage smoothness in the dose-response curves whi…
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation
Shanghai Medical College, Fudan University（复旦大学上海医学院）；Shanghai Institute of Stem Cell Research and Clinical Translation（上海干细胞研究与临床转化研究院）；Shanghai World Foreign Language Academy（上海世界外国语中学）
We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment. Our model learns drug representations at multiple scales based on a drug-protein interaction network and metadata. Structural properties of compounds and proteins are encoded to create vertex features for a message-passing scheme that operates on the bipartite interaction graph. Propagated messages form multi-resolution drug representations which we utilized to create drug pair descriptors. By conditioning the drug c…
IBM Research（IBM研究院）；University of Melbourne（墨尔本大学）；Cures Within Reach for Cancer（癌症可及疗法组织）；Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）
More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs worldwide. Evidence on the efficacy of non-cancer generic drugs being tested for cancer exists in scientific publications, but trying to manually identify and extract such evidence is intractable. In this paper, we introduce a system…
Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cancer drugs, as well as methods for generation of potential drug candidates. However, a concept of generation of compounds with specific properties and simultaneous modeling of their efficacy against cancer cell lines has not been comprehensively explored. To address this need, we present VADEERS, a Variational Autoencoder-based Drug Efficacy Estimation Recommender System. The generation of compound…
Gwangju Institute of Science and Technology（光州科学技术院）
Personalized medicine is expected to maximize the intended drug effects and minimize side effects by treating patients based on their genetic profiles. Thus, it is important to generate drugs based on the genetic profiles of diseases, especially in anticancer drug discovery. However, this is challenging because the vast chemical space and variations in cancer properties require a huge time resource to search for proper molecules. Therefore, an efficient and fast search method considering genetic profiles is required for de novo molecular design of anticancer drugs. Here, we propose a faster mo…
European Bioinformatics Institute（欧洲生物信息学研究所）；Wellcome Trust Sanger Institute（维康桑格研究所）；Massachusetts General Hospital Cancer Center（麻省总医院癌症中心）
Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approach…
Institute of Molecular Systems Biology, ETH Zürich（苏黎世联邦理工学院分子系统生物学研究所）
In this work, we propose two novel methodologies to study temporal and morphological phenotypic effects caused by different experimental conditions using imaging data. As a proof of concept, we apply them to analyze drug effects in 2D cancer cell cultures. We train a convolutional autoencoder on 1M images dataset with random augmentations and multi-crops to use as feature extractor. We systematically compare it to the pretrained state-of-the-art models. We further use the feature extractor in two ways. First, we apply distance-based analysis and dynamic time warping to cluster temporal pattern…
Argonne National Laboratory（阿贡国家实验室）；University of Chicago（芝加哥大学）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Leidos Biomedical Research, Inc.（Leidos生物医学研究公司）
Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating data, a common question is whether the proposed predictors can further improve the generalization performance with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four drug screening datasets. The learning curves a…
Identifying multi-omics interactions for lung cancer drug targets discovery using Kernel Machine Regression
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Dhaka, Bangladesh（信息与通信技术系，穆拉纳巴沙尼科学与技术大学）；Department of Computer Science, Tulane University, New Orleans, LA, USA（计算机科学系，杜兰大学）；Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 138632, Singapore（生物信息学研究所（BII），科技研究局（A*STAR））；Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA（杜兰大学生物医学信息学与基因组学中心，医学部）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（Ochsner研究中心，Ochsner研究）
Background. The Drug Discovery Unit (DDU) of Cancer Research UK (CRUK) is using the software Dotmatics for storage and analysis of scientific data during drug discovery process. Whilst the data include event logs, time stamps, activities, and user information are mostly sitting in the database without fully utilising their potential value. Aims. This dissertation aims at extracting knowledge from event logs data which recorded during drug discovery process, to capture the operational business process of the DDU of Cancer Research UK (CRUK) as it was being executed. It provides the evaluations …
Permutation invariant multi-output Gaussian Processes for drug combination prediction in cancer
MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单元）；Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo（奥斯陆大学奥斯陆生物统计学与流行病学中心）；Broad Institute of MIT & Harvard（麻省理工学院与哈佛大学博德研究所）；CRUK Cambridge Centre Ovarian Programme（英国癌症研究剑桥中心卵巢项目）；Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID)（剑桥治疗免疫学与传染病研究所）
Drug Selection via Joint Push and Learning to Rank
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Indiana University - Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校）
In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amou…
Texas Tech University（德克萨斯理工大学）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）
Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network) based models have shown promising results in drug sensitivity prediction. The primary idea behind REFINED CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from convolutional neural network architectures. However, the mapping from a vector to a compact 2D image is not unique due to var…
Evaluating the Effectiveness of Artificial Intelligence in Predicting Adverse Drug Reactions among Cancer Patients: A Systematic Review and Meta-Analysis
National Polytechnic School of Oran - Maurice Audin（奥兰国家理工学院 - 莫里斯·奥丹）
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
University of Virginia（弗吉尼亚大学）；McGill University（麦吉尔大学）
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics. Recent efforts in generation of large scale datasets profiling gene expression and drug sensitivity in cell lines have provided a unique opportunity to study this problem. However, one major challenge is the small number of samples (cell lines) compared to the number of features (genes) even in these large datasets. We propose a collaborative filtering (CF) like algorithm for modeling gene-drug relationship to identify patients most likely to benefit from a treatment. Due to the correlation of gene ex…
Central South University（中南大学）；Nanjing Tech University（南京工业大学）；Xinjiang University（新疆大学）
Drug combination therapy has become a increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network have recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. In this paper, we proposed a deep learning model based on graph neural networks…
Simon Fraser University（西蒙弗雷泽大学）；Vancouver Prostate Centre（温哥华前列腺中心）；University of British Columbia（不列颠哥伦比亚大学）
In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimplification of the problem, and those methods are typically unable to handle cold-target problems, i.e., problems involving target protein that never appeared in the training set. Towards this, we contrived PADME (Protein And Drug Molecule interaction prEdiction), a framework base…
Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction
The University of Tokyo（东京大学）；Graduate School of Frontier Sciences（前沿科学研究生院）；Laboratory for Medical Science（医学科学实验室）；Department of Computational Biology and Medical Sciences（计算生物学与医学科学系）；Department of Biological Sciences（生物科学系）；School of Science（科学学校）
Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
Texas A&M University（德克萨斯农工大学）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究所）；Leidos Biomedical Research（莱多斯生物医学研究所）；Brookhaven National Laboratory（布鲁克海文国家实验室）
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）
Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models require uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Methods that combine Bayesian models with NN models address this issue, but are difficult to implement and more expensive to train. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can co…
VQSynery: Robust Drug Synergy Prediction With Vector Quantization Mechanism
School of Data Science and Engineering（数据科学与工程学院）；East China Normal University（华东师范大学）；School of Medicine（医学院）；College of Design&Engineering（设计与工程学院）；National University of Singapore（新加坡国立大学）
IBM Research Zurich（IBM苏黎世研究中心）；ETH Zurich（苏黎世联邦理工学院）
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and properties of the target disease. Here, we introduce the first generative model capable of tailoring anticancer compounds for a specific biomolecular profile. Using a RL framework, the transcriptomic profiles of cancer cells are used as a context for the generation of candidate molec…
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
Predicting gene essentiality and drug response from perturbation screens in preclinical cancer models with LEAP: Layered Ensemble of Autoencoders and Predictors
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…
Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models
Department of Computer Science and Engineering（计算机科学与工程系）；University of Notre Dame（诺克斯大学）；Department of Chemistry and Biochemistry（化学与生物化学系）；Lucy Family Institute for Data & Society（数据与社会学院）
