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Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles
This work reported for the first time, multi-condition model based on quantitative structure–activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design of new drug-like molecules with potential pan-antiviral activity (against SARS-Cov-1, SARS, Cov-2, influenza A and B viruses, and RSV) while also exhibiting anti-cytokine storm profiles (against caspase-1 and TNF-alpha).
PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors
Here, we reported for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules with acceptable drug-likeness that can simultaneously inhibit multiple pancreatic cancer (PANC) cell lines as well as the PANC-related proteins named caspase-1, TNF-alpha, and the insulin-like growth factor 1 receptor (IGF1R).
QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites
In this study, we reported the first multi-target model based on quantitative structure-activity relationships and a multilayer perceptron neural network (mt-QSAR-MLP) to virtually design and predict versatile drug-like inhibitors of proteins involved in the survival and/or infectivity of four different pathogenic parasites, namely Trypanosoma cruzi, Trypanosoma brucei brucei, Plasmodium falciparum, and Toxoplasma gondii. Docking calculations converged with the mt-QSAR-MLP model regarding the multi-target profile of the designed molecules.
SOFTWARE & DATABASES
This includes a list of software and databases used in my works as well as others of great importance in the context of computer-aided molecular design, chemoinformatics, and related scientific disciplines.