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

Random Forests

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Analyzing Learned Molecular Representations for Property Prediction

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The rise of deep learning in drug discovery

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Deep patient: An unsupervised representation to predict the future of patients from the electronic health records

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Molecular graph convolutions: moving beyond fingerprints

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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DeepTox: toxicity prediction using deep learning

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Machine learning in materials science

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Predicting reaction performance in C–N cross-coupling using machine learning

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Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

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QSAR without borders

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Deep learning for computational chemistry

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State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

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Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey

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Low Data Drug Discovery with One-Shot Learning

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From machine learning to deep learning: progress in machine intelligence for rational drug discovery

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Deep Learning in Drug Discovery

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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

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Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

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

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Exploiting machine learning for end-to-end drug discovery and development

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Deep Learning in Chemistry

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Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

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Convolutional embedding of attributed molecular graphs for physical property prediction

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Deep learning in microscopy image analysis: a survey

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Big data and artificial intelligence modeling for drug discovery

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Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

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Application of Generative Autoencoder in <i>De Novo</i> Molecular Design

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Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

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The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

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Deep learning in drug discovery: opportunities, challenges and future prospects

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Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

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Is Multitask Deep Learning Practical for Pharma?

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Big data in basic and translational cancer research

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Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

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A renaissance of neural networks in drug discovery

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Discovery and Optimization of Materials Using Evolutionary Approaches

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Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

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Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma

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Artificial intelligence in chemistry and drug design

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Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

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DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

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Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction

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Design of efficient computational workflows for in silico drug repurposing

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tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes

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Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

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Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

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Artificial intelligence facilitates drug design in the big data era

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Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

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Deep Learning to Predict Patient Future Diseases from the Electronic Health Records

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Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

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Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity

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Toward Interpretable Machine Learning Models for Materials Discovery

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Machine Learning in Drug Discovery

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Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction

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Chinese medicine prescription recommendation using generative adversarial network

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