Deep learning for molecular design—a review of the state of the art
@inproceedings{Elton2019DeepLF, title={Deep learning for molecular design—a review of the state of the art}, author={Daniel C. Elton and Zois Boukouvalas and Mark D. Fuge and Peter W. Chung}, year={2019} }
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…
137 Citations
Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning
- Computer SciencebioRxiv
- 2022
This work proposes a new curriculum learning-inspired, recurrent Iterative Optimisation Procedure that enables the optimisation of generated molecules for seen and unseen molecular profiles and allows the user to control whether a molecular profile is explored or exploited.
Deep learning Enabled Molecule Design
- Computer Science
- 2021
An interpretable deep learning method based on graph networks that accurately predict solvation free energies of small organic molecules and the CIGIN model outperforms all the proposed machine learning methods.
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design
- Computer ScienceArXiv
- 2021
This survey includes the theoretical development of the previously mentioned AI models and detailed summaries of 42 recent applications of AI in drug design, and provides a holistic discussion of the abundant applications so that the tasks, potential solutions, and challenges in AI-based drug design become evident.
Discovery of novel chemical reactions by deep generative recurrent neural network
- Computer ScienceScientific reports
- 2021
It is shown that “creative” AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class.
Diversity oriented Deep Reinforcement Learning for targeted molecule generation
- Computer ScienceJournal of Cheminformatics
- 2021
The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction, and it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.
Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)
- Computer Science
- 2019
This work proposes a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN) that is able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders.
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
- Computer ScienceICML
- 2020
A novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de noVO drug design system.
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
- Computer ScienceArXiv
- 2022
This paper systematically review the most relevant work in machine learning models for molecule design and summarizes all the existing molecule design problems into several venues according to the problem setup, including input, output types and goals.
Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics
- Computer ScienceArXiv
- 2020
This work proposes a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA).
Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation
- Computer ScienceFrontiers in Materials
- 2022
This work reviews recent innovations that have enabled GMs to accelerate inorganic materials discovery, focusing on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.
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