José Antonio Sigüenza

José Antonio Sigüenza

jasiguen@espol.edu.ec Download CV

About Me

As a Chemical Engineer with a strong foundation in computer science, I am dedicated to applying artificial intelligence (AI) to advance life sciences, particularly drug discovery. My research focuses on molecular simulations (docking & dynamics), generative modeling, and protein-ligand interactions within computational drug discovery and materials science.


I hold a B.Sc. in Chemical Engineering from ESPOL, Ecuador, and strengthened my computer science skills through coursework at UC Berkeley. My experience includes developing molecular simulation workflows and energy prediction models at Deep Forest Sciences and CIDNA (Ecuador), and contributing to open-source projects like DeepChem. Currently, I am expanding equivariant capabilities for molecular data in DeepChem, specifically developing SE(3)-Transformers.

Publications

Published

  • "DeepChem-Variant: A Modular Open Source Framework for Genomic Variant Calling" — Championing Open-source DEvelopment in Machine Learning (CODEML) Workshop at ICML - 2025.
    PDF
  • "Open-Source Molecular Processing Pipeline for Generating Molecules" — Machine Learning and Physical Sciences (ML4PS) Workshop at NeurIPS, Molecular Machine Learning Conference (MoML), BayLearn 2024.
    arXiv
  • "Molecular docking of triazole-based ligands with KDM5A to identify potential inhibitors" — International Workshop on Innovative Simulation for Healthcare (IWISH) Workshop at I3M 2024.

    IWISH 2024 Best Paper Award.

    PDF, Scopus

Accepted

"Exergoeconomic Analysis and Multi-objective Optimization of Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm" — E2DT 2025. Link not yet available

Under Review

"Synthesis, characterization, and mechanical property prediction of microcellulose-reinforced polyvinyl alcohol composites" — Polymer Journal, 2024. Link not yet available

Other Works

Projects

Normalizing Flows Implementation in Deepchem

Used to generate new molecules using nonlinear transformations. Based on datasets like QM9.

Source

BERT-Powered Question Answering for ESPOL

A chatbot built with BERT and web scraping to answer ESPOL-related questions in real time.

GitHub

Essential Oils Optimization

Optimization of essential oil production using AI models and regression.

Article