Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation

The ANI-1x neural network potential, trained on the density functional theory data set, as a quantum-level machine learning calculation has been investigated to forecast the potential energy surfaces of the Resveratrol (3,5,4′-trihydroxy- trans -stilbene) antiparkinsonian drug in a very short computing time. A comprehensive validation of the ANI-1x deep learning technique was provided on the Resveratrol molecule using density functional theory at the wB97X/6-31G(d) level of theory. The results showcased in this study will offer significant insights into pharmaceutical computational research, medicinal chemistry, drug discovery and design, thereby making a valuable contribution.