Books
Projects
Citation
Editorships
SFSXplorer
Highlights
SFSXplorer (Scoring Function Space Explorer) is a Python package to explore the concept of Scoring Function Space (SFS). We can explore the SFS to build a computational model targeted to a specific protein system (targeted-scoring function). SFSXplorer employs binding affinity data and protein-ligand structures (docked or crystallographic) to train machine learning models to predict binding affinity. We base this SFS exploration on a scoring function with variable energy terms. This scoring function is a polynomial equation with terms accounting for van der Waals, hydrogen bonds, electrostatic, desolvation entropy, and torsional contributions. For the hydrogen-bond energy term, we do not focus on 12/10 potential only. SFSXplorer implements an n/m potential equation. We have the same flexibility for van der Waals terms. SFSXplorer calculates energy terms varying the exponents n and m. For electrostatic potential, we modify the permittivity function. We also have a flexible expression for the desolvation entropy term. We account for the torsional energy by employing the standard potential based on the number of torsion angles. Then, we may choose the set of energy terms with the best predictive performance. We have the flexibility for energy terms making available unexplored regions of the SFS. Dr. Walter F. de Azevedo Jr. proposed the initial idea of SFSXplorer, which now has an international team of scientists participating in its development and testing of SFSXplorer.
Funding
The Brazilian National Council for Scientific and Technological Development (CNPq) (Process 306298/2022-8) supports this research project.