Reacción a "Nobel Prize in Chemistry for Baker, Hassabis and Jumper for computational protein design and structure prediction"

These awards recognize what has become the most significant advance in Artificial Intelligence. The methods implemented by Demis Hassabis and John M. Jumper for protein structure prediction from their sequence - AlphaFold based on DeepNN - have become an indispensable resource in biotechnology and biomedicine. A fundamental difference with respect to other AI developments, for example, the popular ChatGTP, is that these structure predictions are accompanied by a confidence index in the quality of the result. No less important is the contribution of David Baker, who for years has been the leader in the field of applying AI to the design of new proteins, with impressive results in the design of proteins with new properties with applications in biotechnology. His research has broken new ground in the exploration of the protein space with practical and scientific implications.

It is noteworthy that David Baker has led both the movement for the open release of the software and for the responsible use of these new technologies. Unfortunately, the work of Demis Hassabis and John M. Jumper represents a less bright page in this regard since, although their first developments (AlphaFold 1 and 2) were open source, the subsequent ones (AlphaFold 3) have not been, creating a great deal of controversy in the scientific community. Modestly, I am very happy to see that my developments of the first methods for obtaining constrictions from sequence alignments (correlated mutations) in the 90's are recognized by the community as the key piece on which these new methods are built.