An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design

Abstract

As a promising tool to navigate in the vast chemical space, artificial intelligence (AI) is leveraged for drug design. From the year 2017 to 2021, the number of applications of several recent AI models (i.e. graph neural network (GNN), recurrent neural network (RNN), variation autoencoder (VAE), generative adversarial network (GAN), flow and reinforcement learning (RL)) in drug design increases significantly. Many relevant literature reviews exist. However, none of them provides an in-depth summary of many applications of the recent AI models in drug design. To complement the existing literature, this survey includes the theoretical development of the previously mentioned AI models and detailed summaries of 42 recent applications of AI in drug design. Concretely, 13 of them leverage GNN for molecular property prediction and 29 of them use RL and/or deep generative models for molecule generation and optimization. In most cases, the focus of the summary is the models, their variants, and modifications for specific tasks in drug design. Moreover, 60 additional applications of AI in molecule generation and optimization are briefly summarized in a table. Finally, this survey provides a holistic discussion of the abundant applications so that the tasks, potential solutions, and challenges in AI-based drug design become evident.

26 pages, 6 figures, 3 tables, 253 references

References in corpus (26)

• Semi-Supervised Classification with Graph Convolutional Networks
• On the difficulty of training Recurrent Neural Networks
• NICE: Non-linear Independent Components Estimation
• Deep learning for molecular design - a review of the state of the art
• Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
• Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
• Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
• Learning Graph-Level Representation for Drug Discovery
• MolecularRNN: Generating realistic molecular graphs with optimized properties
• A Two-Step Graph Convolutional Decoder for Molecule Generation
• Equivariant Graph Neural Networks for 3D Macromolecular Structure
• Hierarchical Inter-Message Passing for Learning on Molecular Graphs
• DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction
• Graph Residual Flow for Molecular Graph Generation
• Decoding Molecular Graph Embeddings with Reinforcement Learning
• JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
• Drug-Target Interaction Prediction with Graph Attention networks
• Message Passing Networks for Molecules with Tetrahedral Chirality
• Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
• Quantum Generative Models for Small Molecule Drug Discovery
• Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
• Artificial Intelligence in Drug Discovery: Applications and Techniques
• Tiered Latent Representations and Latent Spaces for Molecular Graphs
• ChemoVerse: Manifold traversal of latent spaces for novel molecule discovery
• A Systematic Assessment of Deep Learning Models for Molecule Generation
• Graph Energy-based Model for Substructure Preserving Molecular Design