The Molecular Modeling Laboratory (LMM) of the State University of Feira de Santana (UEFS) took part in the VI Brazilian Symposium on (Bio)pharmaceutical Innovation and the VIII Bahia Symposium on (Bio)pharmaceutical Innovation (SIMBIFAR 2026), held on October 20–22 at the State University of Bahia (UNEB), Campus I, in Salvador (BA). The event was organized by the Graduate Program in Pharmaceutical Sciences (PPGFARMA) of the Department of Life Sciences (DCV) of UNEB. The group’s presentations took place on October 21.

The laboratory was represented by undergraduate research students Laiane Santos França Costa and Caroline Souza da Anunciação, and by graduate students Pedro Henrique Leite Silva and Késsia Souza Santos, of the Graduate Program in Pharmaceutical Sciences (PPGCF), and Ylanna Bonfim dos Santos, of the Graduate Program in Biotechnology, accompanied by the LMM coordinator, professor Manoelito Coelho dos Santos Junior. In total, the group presented one oral talk and two posters.

Oral presentation

Pedro Henrique Leite Silva presented the work “Construction and validation of a molecular target database for inverse virtual screening”. In inverse virtual screening, instead of searching for molecules for a target, one starts from a molecule and looks, among many targets, for those it may interact with. To this end, the work organized and validated a set of protein structures that will serve as the basis for this type of study.

Poster presentations

AhR receptor modulators from natural products

The poster “Hierarchical virtual screening for the identification of aryl hydrocarbon receptor modulators from natural sources”, by Caroline Souza da Anunciação, Ylanna Bonfim dos Santos and Manoelito C. Santos Junior, sought new modulators of the aryl hydrocarbon receptor (AhR), associated with tumor progression. The study combined pharmacophore models, screening of a natural products database, molecular docking and molecular dynamics simulations to select the most promising compounds.

Artificial intelligence against tuberculosis

The poster “Use of artificial intelligence to prioritize molecules for the treatment of Mycobacterium tuberculosis”, by Laiane Santos França Costa, Késsia Souza Santos and Manoelito C. Santos Junior, integrated artificial intelligence and virtual screening to prioritize molecules with potential to inhibit the DprE1 enzyme of Mycobacterium tuberculosis. The work used K-talysticFlow, a deep learning tool developed at LMM, together with pharmacophore modeling and molecular docking, making the search for new candidates more targeted.

Science made in Bahia

Taking part in SIMBIFAR reinforces the role of LMM in training students at all levels and in producing research in computer-aided drug discovery. The laboratory thanks PPGFARMA/UNEB for organizing the event and the agencies and programs that support its research, including CAPES, CNPq, PPPG/UEFS, PPGCF and the Graduate Program in Biotechnology.

Image: SIMBIFAR 2026 banner (IV SIMBIFAR/PPGFARMA/UNEB).