Applications of machine learning in drug discovery and development

Deep learning

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Untitled work

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Reducing the dimensionality of data with neural networks

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A guide to deep learning in healthcare

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Defining a Cancer Dependency Map

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MoleculeNet: a benchmark for molecular machine learning

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Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

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Relating protein pharmacology by ligand chemistry

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The ChEMBL database in 2017

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A Landscape of Pharmacogenomic Interactions in Cancer

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Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

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Planning chemical syntheses with deep neural networks and symbolic AI

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The rise of deep learning in drug discovery

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The support of human genetic evidence for approved drug indications

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Estimation of clinical trial success rates and related parameters

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Deep learning for computational biology

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Molecular de-novo design through deep reinforcement learning

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Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

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Convergence of Acquired Mutations and Alternative Splicing of CD19 Enables Resistance to CART-19 Immunotherapy

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The human splicing code reveals new insights into the genetic determinants of disease

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Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

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The molecular classification of multiple myeloma

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DeepTox: toxicity prediction using deep learning

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Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

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A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1

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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

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The BATTLE Trial: Personalizing Therapy for Lung Cancer

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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

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Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival

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A community effort to assess and improve drug sensitivity prediction algorithms

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Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

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Deep Learning—A Technology With the Potential to Transform Health Care

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Deep learning based tissue analysis predicts outcome in colorectal cancer

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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

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ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

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Artificial intelligence faces reproducibility crisis

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Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

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Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

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Open Targets: a platform for therapeutic target identification and validation

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A new view of transcriptome complexity and regulation through the lens of local splicing variations

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Deep learning of the tissue-regulated splicing code

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Impact of a five-dimensional framework on R&D productivity at AstraZeneca

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Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib.

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Massively Multitask Networks for Drug DiscoveryPreprint

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Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome

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Deep learning for classification of colorectal polyps on whole-slide images

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Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery

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Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

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Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

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Properties and identification of human protein drug targets

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Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology

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MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

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Performance measures in evaluating machine learning based bioinformatics predictors for classifications

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On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites

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Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research

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Is Multitask Deep Learning Practical for Pharma?

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Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

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A call for deep-learning healthcare

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Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer

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Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

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Characterisation of mental health conditions in social media using Informed Deep Learning

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Biomarkers as drug development tools: discovery, validation, qualification and use

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Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

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Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission

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Significance and implications of FDA approval of pembrolizumab for biomarker-defined disease

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VASC: Dimension Reduction and Visualization of Single-Cell RNA-Seq Data by Deep Variational Autoencoder

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A multi-scale convolutional neural network for phenotyping high-content cellular images

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A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

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Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers

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Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer

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A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

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In silico prediction of novel therapeutic targets using gene–disease association data

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ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions

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Open source machine-learning algorithms for the prediction of optimal cancer drug therapies

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Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology

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Prediction of potential drug targets based on simple sequence properties

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Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

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Transforming Computational Drug Discovery with Machine Learning and AI

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Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

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Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic

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Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images

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Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer

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An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

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Using Information from Historical High-Throughput Screens to Predict Active Compounds

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Toxicity Prediction using Deep LearningPreprint

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A machine-learning heuristic to improve gene score prediction of polygenic traits

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A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

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