Deep learning for molecular design - a review of the state of the art

Abstract

In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules - in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.

24 pages, new title, published in RSC MSDE

References in corpus (14)

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• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
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Cited by in corpus (50)

• Graph Representation Learning in Biomedicine
• Perspective on integrating machine learning into computational chemistry and materials science
• Inverse design of 3d molecular structures with conditional generative neural networks
• Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
• Regression Transformer: Concurrent sequence regression and generation for molecular language modeling
• Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
• Diffusion Models in $\text{De Novo}$ Drug Design
• Graph Neural Networks for Molecules
• High-throughput property-driven generative design of functional organic molecules
• Machine learning-assisted design of material properties
• Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
• Physically inspired deep learning of molecular excitations and photoemission spectra
• Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
• Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?
• Generative Language Model for Catalyst Discovery
• Graph Machine Learning for Design of High-Octane Fuels
• The Expressive Power of Graph Neural Networks: A Survey
• Graph neural networks for the prediction of molecular structure-property relationships
• From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
• Controllable Data Generation by Deep Learning: A Review
• Acoustic Cloak Design via Machine Learning
• Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
• Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
• Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
• Message Passing Networks for Molecules with Tetrahedral Chirality
• Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions
• Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
• Automated patent extraction powers generative modeling in focused chemical spaces
• IMG2SMI: Translating Molecular Structure Images to Simplified Molecular-input Line-entry System
• Artificial Intelligence in Drug Discovery: Applications and Techniques
• Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning
• Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
• pyscreener: A Python Wrapper for Computational Docking Software
• Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning
• G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning
• Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
• Prospecting for Pluripotency in Metamaterial Design
• Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research
• Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective
• An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design
• Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties
• Applying Deutsch's concept of good explanations to artificial intelligence and neuroscience -- an initial exploration
• Constrained Discrete Black-Box Optimization using Mixed-Integer Programming
• Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds
• Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening
• Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
• Augmenting Molecular Images with Vector Representations as a Featurization Technique for Drug Classification
• Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
• Continuous Representation of Molecules Using Graph Variational Autoencoder
• Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center