Our technical publications examine the intersection of exascale computing, molecular simulation, and ethical AI implementation.
Computational evaluation of 34 metabolites from Andean Schinus molle essential oil against the sterol carrier protein SCP-2 (PDB: 1PZ4) of Aedes aegypti, using molecular docking with AutoDock Vina and molecular dynamics in GROMACS.
Docking and molecular dynamics study of nine glucosinolates distinctive to Peruvian maca varieties against human acetylcholinesterase (PDB: 4EY6), benchmarked against galantamine as reference drug, to prioritize neuroprotective candidates.
Development of an automated pipeline for protein docking that reduces processing time by 70% while maintaining over 90% accuracy. Methodology validated with standard PDB benchmarks.
Implementation of a convolutional neural network trained on 50,000 genomes to identify variants of uncertain significance in non-coding regions. 94% accuracy in cross-validation.
Infrastructure proposal combining cloudburst resources with on-premise clusters, reducing operational costs by 35% for simulation workloads.
Open-source tool that standardizes genomic analysis pipeline execution, ensuring full reproducibility and traceability from raw data to results.
Segmentation model trained on 100K+ microscopy images that identifies subcellular structures with an F1-score of 0.96, outperforming traditional methods.
Performance comparison between classical and quantum solvers (QAOA) on combinatorial optimization problems, with scalability analysis and quantum advantage conditions.
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