High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning

• DOI: 10.1021/acs.jcim.0c00273
• Publication Date: 2020-06-04T17:36:07Z
• Authors: Koichiro Kato; Tomohide Masuda; Chiduru Watanabe; Naoki Miyagawa; Hideo Mizouchi; Shumpei Nagase; Kikuko Kamisaka; Kanji Oshima; Satoshi Ono; Hiroshi Ueda; Atsushi Tokuhisa; Ryo Kanada; Masateru Ohta; Mitsunori Ikeguchi; Yasushi Okuno; Kaori Fukuzawa; Teruki Honma
• Keywords: Machine Learning; Drug Design; Proteins; Neural Networks, Computer; Molecular Dynamics Simulation; 01 natural sciences; 0104 chemical sciences

Here, we have constructed neural network-based models that predict atomic partial charges with high accuracy at low computational cost. The models were trained using high-quality data acquired from quantum mechanics calculations using the fragment molecular orbital method. We have succeeded in obtaining highly accurate atomic partial charges for three representative molecular systems of proteins, including one large biomolecule (approx. 2000 atoms). The novelty of our approach is the ability to take into account the electronic polarization in the system, which is a system-dependent phenomenon, being important in the field of drug design. Our high-precision models are useful for the prediction of atomic partial charges and expected to be widely applicable in structure-based drug designs such as structural optimization, high-speed and high-precision docking, and molecular dynamics calculations.