RESUMO
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
Assuntos
Aprendizado de Máquina , Proteínas , Proteínas/química , Proteínas/metabolismo , Ligantes , Software , Simulação de Acoplamento MolecularRESUMO
BACKGROUND: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. OBJECTIVE: Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. METHOD: We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. RESULTS: The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. CONCLUSION: The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.
RESUMO
Natural products and their secondary metabolites are promising starting points for the development of drug prototypes and new drugs, as many current treatments for numerous diseases are directly or indirectly related to such compounds. State-of-the-art, curated, integrated, and frequently updated databases of secondary metabolites are thus highly relevant to drug discovery. The SistematX Web Portal, introduced in 2018, is undergoing development to address this need and documents crucial information about plant secondary metabolites, including the exact location of the species from which the compounds were isolated. SistematX also allows registered users to log in to the data management area and gain access to administrative pages. This study reports recent updates and modifications to the SistematX Web Portal, including a batch download option, the generation and visualization of 1H and 13C nuclear magnetic resonance spectra, and the calculation of physicochemical (drug-like and lead-like) properties and biological activity profiles. The SistematX Web Portal is freely available at http://sistematx.ufpb.br.
Assuntos
Produtos Biológicos , Bases de Dados Factuais , Descoberta de Drogas , Espectroscopia de Ressonância Magnética , PlantasRESUMO
Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.