Stochastic machine learning via sigma profiles to build a digital chemical space.

ABSTRACT: This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate

SUBMITTER: Abranches DO 

PROVIDER: S-EPMC11295021 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature