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Brainstorming: weighted voting prediction of inhibitors for protein targets

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Deep learning approaches to problems in speech recognition, computational chemistry, and natural language text processing

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Harnessing Chaos in Recurrent Neural Networks

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Physicochemical Effects in the Representation of Molecular Structures for Drug Designing

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De novo Molecular Design

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Design of MHC I stabilizing peptides by agent-based exploration of sequence space

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Constructing a Foundational Platform Driven by Japan’s K Supercomputer for Next‐Generation Drug Design

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An introduction to bio-inspired artificial neural network architectures.

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Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems

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Studying the Explanatory Capacity of Artificial Neural Networks for Understanding Environmental Chemical Quantitative Structure−Activity Relationship Models

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Structure optimization of an artificial neural filter detecting membrane-spanning amino acid sequences

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Exhaustive Proteome Mining for Functional MHC-I Ligands

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Swarm intelligence metaheuristics for enhanced data analysis and optimization

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Coping with Polypharmacology by Computational Medicinal Chemistry

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Compact Extreme Learning Machines for biological systems

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The Deep Model

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Opportunities and obstacles for deep learning in biology and medicine

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Deep Learning for Health Informatics

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The rise of deep learning in drug discovery

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Drug discovery with explainable artificial intelligence

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<scp>eD</scp> octor: machine learning and the future of medicine

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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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Automating drug discovery

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QSAR without borders

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Deep learning for computational chemistry

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The role of AI in drug discovery: challenges, opportunities, and strategies

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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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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

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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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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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Big data and artificial intelligence modeling for drug discovery

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Deep learning in mental health outcome research: a scoping review

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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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Recurrent Neural Network Model for Constructive Peptide Design

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Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

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Deep learning for electronic health records: A comparative review of multiple deep neural architectures

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Artificial intelligence for aging and longevity research: Recent advances and perspectives

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A renaissance of neural networks in drug discovery

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An exposome perspective: Early-life events and immune development in a changing world

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Deep learning in pharmacogenomics: from gene regulation to patient stratification

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Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

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

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A bird's-eye view of deep learning in bioimage analysis

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Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

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

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Autonomous Molecular Design: Then and Now

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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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Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

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

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Machine Learning in Drug Discovery

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Formatting biological big data for modern machine learning in drug discovery

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Introduction to cheminformatics for green chemistry education

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Cheminformatics in Drug Discovery, an Industrial Perspective

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