JSAI2024
May 28 - May 31, 2024ACTCITY Hamamatsu + Online
JSAI2024
May 28 - May 31, 2024ACTCITY Hamamatsu + Online
[4Q3-IS-2d-05]A machine learning model for predicting quantum chemistry based protein-drug molecule interactions
〇Ryosuke Kita1, Chiduru Watanabe2, Masateru Ohta2, Naoki Tanimura3, Koji Okuwaki4, Mitsunori Ikeguchi2,5, Kaori Fukuzawa6, Teruki Honma2, Tsuyohiko Fujigaya1, Koichiro Kato1(1. Department of Applied Chemistry, Kyushu University, 2. RIKEN Center , 3. Mizuho Research & Technologies, Ltd, 4. JSOL Corporaion, 5. Yokohama City University, 6. Department of Pharmacy, Osaka University)
Keywords:
Computational Chemistry,AI Drug Discovery,Fragment Molecular Orbital Method
The evaluation of protein-drug molecule interactions through molecular simulation plays a critical role in identifying drug candidates from a vast pool of chemical compounds in computational drug discovery.
To increase the hit rate, which has been a challenge with traditional methods, accurate quantum chemical calculations of protein-drug molecule interactions are necessary.
However, evaluating protein-drug molecule interactions using conventional quantum chemistry calculation methods is challenging.
The Fragment Molecular Orbital (FMO) method allows for the calculation of protein-drug molecule interactions with quantum chemistry accuracy.
Still, even using the "Fugaku" supercomputer, it takes several hours per structure, indicating a need for further reduction in computational costs.
This study presents a machine learning model that predicts interaction values between proteins and drug molecules using the FMO method.
This model is based on a neural network and utilizes vectors of the surrounding environment of each atom in the drug molecule as explanatory variables.
Using a dataset of approximately 2000 structures, the model was trained and tested for predicting interactions in unknown structures.
The model successfully predicted protein-drug molecule interactions with an R2 value of 0.59.
To increase the hit rate, which has been a challenge with traditional methods, accurate quantum chemical calculations of protein-drug molecule interactions are necessary.
However, evaluating protein-drug molecule interactions using conventional quantum chemistry calculation methods is challenging.
The Fragment Molecular Orbital (FMO) method allows for the calculation of protein-drug molecule interactions with quantum chemistry accuracy.
Still, even using the "Fugaku" supercomputer, it takes several hours per structure, indicating a need for further reduction in computational costs.
This study presents a machine learning model that predicts interaction values between proteins and drug molecules using the FMO method.
This model is based on a neural network and utilizes vectors of the surrounding environment of each atom in the drug molecule as explanatory variables.
Using a dataset of approximately 2000 structures, the model was trained and tested for predicting interactions in unknown structures.
The model successfully predicted protein-drug molecule interactions with an R2 value of 0.59.