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Advanced machine learning methods impacted not only the prediction of properties for small molecules and quantitative structure− activity relationships 1 but also the prediction of protein structures. Since its unveiling at the 14th Critical Assessment of protein Structure Prediction (CASP14), AlphaFold2 (AF2) 2 is widely considered as a major breakthrough in protein structure prediction due to the high accuracy it achieved. AF2 uses the primary sequence of the protein as input and, employing a combination of multiple sequence alignment and neural networks, it returns as output a prediction of the protein structure and a confidence score per residue, indicating how reliable the model is. Apart from the code available online, there is also the AlphaFold database, 3 with predictions available for human proteins and proteins from other organisms. Thanks to AF2, computational chemists can now have access to structures …