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Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 p\n                    <jats:italic>K<\/jats:italic>\n                    units and a Pearson\u2019s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks, for which it has not been explicitly trained. Therefore, we show that the model can be combined with the classical scoring function AutoDock Vina in the context of\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\Delta$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u0394<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\Delta$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u0394<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -AEScore has an RMSE of 1.32 p\n                    <jats:italic>K<\/jats:italic>\n                    units and a Pearson\u2019s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the docking and screening power of the underlying classical scoring function.\n                  <\/jats:p>","DOI":"10.1186\/s13321-021-00536-w","type":"journal-article","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T04:03:02Z","timestamp":1628913782000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Learning protein-ligand binding affinity with atomic environment vectors"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2845-3410","authenticated-orcid":false,"given":"Rocco","family":"Meli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3017-8307","authenticated-orcid":false,"given":"Andrew","family":"Anighoro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5204-5508","authenticated-orcid":false,"given":"Mike J.","family":"Bodkin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1731-8405","authenticated-orcid":false,"given":"Garrett M.","family":"Morris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5100-8836","authenticated-orcid":false,"given":"Philip C.","family":"Biggin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"issue":"1","key":"536_CR1","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1039\/c5sc02678d","volume":"7","author":"M Aldeghi","year":"2016","unstructured":"Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC (2016) Accurate calculation of the absolute free energy of binding for drug molecules. 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