Deep reinforcement learning in chemistry: A review.

Reinforcement learning (RL) has been applied to various domains in computational chemistry and has found wide-spread success. In this review, we first motivate the application of RL to chemistry and list some broad application domains, for example, molecule generation, geometry optimization, and retrosynthetic pathway search. We set up some of the formalism associated with reinforcement learning that should help the reader translate their chemistry problems into a form where RL can be used to solve them.

MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning.

Most optimization problems require the user to select an algorithm and, to some extent, also tune it for better performance. Although intuition and knowledge about the problem can speed up these selection and fine-tuning processes, users often use trial-and-error methodologies, which can be time-consuming and inefficient. With all of that in mind and much more, the concept of "learned optimizers", "learning to learn", and "meta-learning" has been gathering attention in recent years.

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications.

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important.

Benchmark study on deep neural network potentials for small organic molecules.

There has been tremendous advancement in machine learning (ML) applications in computational chemistry, particularly in neural network potentials (NNP). NNPs can approximate potential energy surface (PES) as a high dimensional function by learning from existing reference data, thereby circumventing the need to solve the electronic Schrödinger equation explicitly. As a result, ML accelerates chemical space exploration and property prediction compared to quantum mechanical methods.

DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers.

Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster.