Abstract We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2. Graphic abstract
【초록키워드】 Mutation, binding affinity, Spike protein, Algorithm, molecular, predict, binding, Abstract, receptor ACE2, high accuracy, applied, subset, several variant, the SARS-CoV-2, 【제목키워드】 SARS-CoV-2 variant, binding,