Open-Source Machine Learning in Computational Chemistry

Abstract

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

DeepKa Web Server: High-Throughput Protein pKa Prediction

“…In addition to structural proteins, it was found that DeepKa is applicable to intrinsically disordered peptides (IDPs), such as NUPR1, sharing high robustness with a physics model like CpHMD . DeepKa is open access and recommended by Alexander Hagg and Karl N. Kirschner in their recent perspective article of open-source machine learning in computational chemistry …”

Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk

“…21–24 Although ML models are still not a complete substitute for expert intuition, 25 they are sufficiently sophisticated to recognize complex patterns beyond the reach of expert intuition to provide decision-making advice for major challenges in science and engineering, as multiple algorithms and different architectures for ML solutions emerge. 26–29…”

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

“…Computer-aided chemistry has taken many forms in recent decades. The use of machine learning (ML) has proliferated in order to drastically reduce design and experimental effort [28][29][30]. Therefore, there is an urgent need to bypass traditional tedious experimental exploration and theoretical calculation processes and combine emerging ML methods with luminescent chemistry to achieve rapid and accurate predictions of luminescent properties from their molecular structures [31][32][33].…”