A machine learning model for predicting quantum chemistry based protein-drug molecule interactions

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<p>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.</p>

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