MACHINE LEARNING TECHNIQUES FOR PREDICTING THERMOSTABILITY

Techniques for computationally screening a set of single-chain variable fragments (scFvs). The techniques include determining, using a machine learning model, a thermostability indication for each scFv in a set of scFvs to obtain a plurality of thermostability indications, the set of scFvs comprising a first scFv having a first residue sequence, the determining comprising: obtaining, using information indicative of a 3D structure of the first scFv, interaction energy metrics for each of a plurality of pairs of residues in the first residue sequence; generating a first set of features using the interaction energy metrics; and providing the first set of features as input to the machine learning model to obtain a corresponding output indicative of a first thermostability for the first scFv; identifying a subset of the set of scFvs for subsequent production based on the plurality of thermostability indications; and producing at least one of the identified scFvs.