Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning

• Published in: Nature communications 2022-03, Vol.13 (1), p.1245-1245, Article 1245
• Main Authors: Alibakhshi, Amin, Hartke, Bernd
• Format: Article
• Language: English
• Subjects: 119/118; 639/638/563/606; 639/638/630; Accuracy; Chemistry; Efficiency; Energy; Enzymes; Free energy; Humanities and Social Sciences; Learning algorithms; Machine Learning; Molecular structure; multidisciplinary; Representations; Science; Science (multidisciplinary); Solvation; Solvents; Solvents - chemistry
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-28912-6

Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of non-covalent interaction energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets. Molecular representations are fundamental tools for machine-learning models. The current work introduces a new set of molecular representations demonstrated to enable accurate predictions of molecular conformational energy and solvation free energy.