Daniel Castaneda Mogollon

Biography

Daniel joined the Cumming School of Medicine, at the University of Calgary as a PhD student in September 2018 working under the supervision of Dr. Dylan Pillai, Department of Microbiology and Infectious Diseases.  Daniel has received a Bachelor’s Degree in Food Engineering and a Master’s in Bioinformatics. He also was a Bioinformatics Consultant at The University of Texas, a Research Intern at IUPUI (Indianapolis, Indiana), and a Teaching Assistant in Statistics and Bioinformatics.

Daniel is currently working with malaria genotyping through variations of msp1 and msp2 sequenced genes (through NGS) of returning travelers with fever.

Daniel’s latest publication is entitled: DLSCORE: A Deep Learning Model for Predicting Protein-Ligand Binding Affinities. Md Mahmudulla Hassan; Daniel Castaneda Mogollon; Olac Fuentes; Suman Sirimulla.

In recent years, the cheminformatics community has seen an increased success with machine learning-based scoring functions for estimating binding affinities and pose predictions. The prediction of protein-ligand binding affinities is crucial for drug discovery research. Many physics-based scoring functions have been developed over the years. Lately, machine learning approaches are proven to boost the performance of traditional scoring functions. In this study, a novel deep learning-based scoring function (DLSCORE) was developed and trained on the refined PDBBind v.2016 dataset using 348 BINding ANAlyzer (BINANA) descriptors. The neural networks of the DLSCORE model have different number of fully connected hidden layers. Our model, an ensemble of 10 networks, yielded a Pearson R2 of 0.82, a Spearman Rho R2 of 0.90, Kendall Tau R2 of 0.74, an RMSE of 1.15 kcal=mol, and an MAE of 0.86 kcal=mol for our test set. This software is available on Github at https://github.com/sirimullalab/dlscore.git