Scientific Contributions

Selected Publications

• FragmentRetro: A Quadratic Retrosynthetic Method Based on Fragmentation Algorithms
  Authors: Shee, Y; Smaldone, AM; Kyro, GW; Batista, VS
  Journal of Chemical Theory and Computation, 2026

• T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment
  Authors: Kyro, GW; Smaldone, AM; Shee, Y; Xu, T; Batista, VS
  Journal of Chemical Information and Modeling, 2025

• CardioGenAI: A Machine Learning-based Framework for Re-engineering Drugs for Reduced hERG Liability
  Authors: Kyro, GW; Martin, MT; Watt, ED; Batista, VS
  Journal of Cheminformatics, 2025, 17, 30

• A Model-Centric Review of Deep Learning for Protein Design
  Authors: Kyro, GW; Qiu, T; Batista, VS
  arXiv

• A Hybrid Transformer Architecture with a Quantized Self-Attention Mechanism Applied to Molecular Generation
  Authors: Smaldone, AM; Shee, Y; Kyro, GW; Farag, MH; Chandani, Z; Kysoeva, E; Batista, VS
  Journal of Chemical Theory and Computation, 2025

• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
  Authors: Smaldone, AM; Kyro, GW; Shee, Y; Batista, VS
  Chemical Reviews, 2025, 125, 12, 5436-5460

• ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
  Authors: Kyro, GW; Morgunov, A; Brent, RI; Batista, VS
  Journal of Chemical Information and Modeling, 2024, 64, 3, 653-665

• Hierarchical Cross-Scale Transformer Architecture for Bottom-Up Reasoning
  Authors: Kyro, GW; Batista, VS
  TechRxiv, 2025

• HAC-Net: A Hybrid Attention-based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction
  Authors: Kyro, GW; Brent, RI; Batista, VS
  Journal of Chemical Information and Modeling, 2023, 63, 7, 1947-1960

• Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning
  Authors: Smaldone, AM; Kyro, GW; Batista, VS
  Quantum Machine Intelligence, 2023, 5, 41

• MDiGest: A Python Package for Describing Allostery from Molecular Dynamics Simulations
  Authors: Maschietto, F; Allen, B; Kyro, GW; Batista, VS
  Journal of Chemical Physics, 2023, 158, 215103

• The Landscape of Computational Approaches for Artificial Photosynthesis
  Authors: Yang, KR; Kyro, GW; Batista, VS
  Nature Computational Science, 2023, 3, 504-513

• Turning Up the Heat Mimics Allosteric Signaling in Imidazole-Glycerol Phosphate Synthase
  Authors: Maschietto, F; Morzan, U; Tofoleanu, F; Gheereart, A; Chaudhuri, A; Kyro, GW; Nekrasov, P; Brooks, B; Loria, JP; Rivalta, I; Batista, VS
  Nature Communications, 2023, 14, 2239

• Mapping N- to C-terminal Allosteric Coupling Through Disruption of the Putative CD74 Activation Site in D-Dopachrome Tautomerase
  Authors: Chen, E; Widjaja, V; Kyro, GW; Allen, B; Das, P; Bhandari, V; Lolis, EJ; Batista, VS; Lisi, GP
  Journal of Biological Chemistry, 2023, 299, 6, 104729

• Electrostatic Networks for Characterization of Allosteric Pathways in Cas9 Apo, RNA- and DNA-Bound Forms
  Authors: Maschietto, F; Kyro, GW; Allen, B; Batista, VS
  Biophysical Journal, 2023, 122 (3)

• Twisting and Swiveling Domain Motions in Cas9 to Recognize Target DNA Duplexes, Make Double-Strand Breaks, and Release Cleaved Duplexes
  Authors: Wang, J; Arantes, PR; Ahsan, M; Sinha, S; Kyro, GW; Maschietto, F; Allen, B; Skeens, E; Lisi, GP; Batista, VS; Palermo, G
  Frontiers in Molecular Biosciences, 2023, 9

• Structural Basis for Reduced Dynamics of Three Engineered HNH Endonuclease Lys-to-Ala Mutants for the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Associated 9 (CRISPR/Cas9) Enzyme
  Authors: Wang, J; Skeens, E; Arantes, P; Maschietto, F; Allen, B; Kyro, GW; Lisi, GP; Palermo, G; Batista, VS
  Biochemistry, 2022, 61 (9), 785-794

• Photophysics of Rhenium(I) Polypyridyl-based Complexes and Their Employment as Highly Sensitive Anion Sensors
  Authors: Kyro, GW; Lees, AJ
  2021

Open-Source Software

Machine Learning Frameworks

• T-ALPHA
  A hierarchical transformer-based deep neural network for protein-ligand binding affinity prediction with uncertainty-aware self-learning for protein-specific alignment. Currently the state-of-the-art model for predicting protein-ligand binding affinity.

• CardioGenAI
  A generative deep learning framework for re-engineering drugs to reduce hERG-related cardiotoxicity while preserving primary pharmacology. Successfully applied in Pfizer R&D programs.

• ChemSpaceAL
  The first active learning methodology for fine-tuning molecular generative models toward specified protein targets. Currently being used in collaboration with Brown University for designing small-molecule binders to CRISPR-Cas9.

• HAC-Net
  A hybrid attention-based convolutional neural network for highly accurate protein-ligand binding affinity prediction. Used to identify potential inhibitors for G protein-coupled receptors and antivirulence drugs.

Computational Biology Tools

• Molecular Dynamics Analyses
  A comprehensive toolkit for analyzing molecular dynamics simulations, with specialized focus on protein allostery.

• Eigenvector Centrality
  A novel method for analyzing information transfer in proteins using eigenvector centrality in protein structure networks. Provides insights into allosteric mechanisms of biological systems including CRISPR-Cas9, imidazole glycerol phosphate synthase, and D-dopachrome tautomerase.

Innovations Acquired by Pharmaceutical Companies

• SAGE Platform
  An innovative platform that incorporates AI, cutting-edge reactions, and automated synthesis to reduce attrition in early-stage drug discovery.
  Acquired by: Merck Group (August 2024)

Selected Presentations & Lectures

• “Industry Trends in AI for Scientific Discovery”. Invited Interview at PhD Pathways Speaker Series, Yale University (2025).
• “Artificial Intelligence for Preclinical Drug Safety”. Invited Round-Table Discussion at Novartis Institutes for Biomedical Research (2025).
• “Modeling Protein-Ligand Interactions and Generative AI for Lead Optimization”. Invited Round-Table Discussion at Novartis Institutes for Biomedical Research (2025).
• “Quantum-Classical Machine Learning Methods for Optimizing Drug Toxicity”. Invited Research Talk at QuantumCT Industry Collaboration Forum, Yale Ventures (2025).
• “Exploring How Quantum Computing Will Impact Pharmaceutical Research”. Invited Technology Panel Discussion at QuantumCT Industry Collaboration Forum, Yale Ventures (2025).
• “Current State-of-the-Art Deep Learning Models for Protein Design”. Invited Research Talk at Biophysical Chemistry Seminar, Yale University (2025).
• “Introduction to Deep Learning for Biochemistry”. Invited Guest Lecture at CHEM 584: Machine Learning and Quantum Computing, Yale University (2025).
• “Transformers for Modeling Protein-Ligand Interactions”. Poster Presentation at Chemical Research Symposium, Yale University (2025).
• “A Hybrid Quantum-Classical Transformer Architecture with a Quantized Self-Attention Mechanism Applied to Molecular Generation”. Poster Presentation at Chemical Research Symposium, Yale University (2025).
• “Applications of Deep Learning in Cardiovascular and Safety Domains”. Invited Round-Table Discussion at Novartis Institutes for Biomedical Research (2025).
• “Machine Learning for Modeling Cardiac Ion Channels”. Invited Research Talk at Scientific Seminar, Novartis Institutes for Biomedical Research (2025).
• “T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment”. Invited Research Talk at 10th Annual Biophysics and Structural Biology Research Symposium, Yale University (2025).
• “Assessment of Different Machine Learning Architectures for hERG Activity Prediction”. Poster Presentation at Computational Medicinal Chemistry School, Novartis Institutes for Biomedical Research (2024).
• “Some of the Problems with Public Datasets for Protein-Ligand Binding Affinity Prediction”. Invited Research Talk at Computational Biophysics and Drug Design Meeting, Bernal Institute at the University of Limerick (2024).
• “Generative AI Methods for Lead Optimization in Drug Discovery”. Poster Presentation at Chemical Research Symposium, Yale University (2024).
• “CardioGenAI: A Machine Learning Framework for Re-engineering Drugs for Reduced hERG Liability”. Invited Research Talk at Innovation Cup Alumni Symposium, Merck Group (2024).
• “A Machine Learning Framework for Re-engineering Drugs for Reduced hERG Liability”. Invited Research Talk at Global Discovery Investigative Toxicology and Translational Sciences—Computational Safety Sciences Town Hall, Groton Laboratories at Pfizer Research & Development (2024).
• “CardioGenAI: A Machine Learning-based Framework for Re-engineering Drugs for Reduced hERG Liability”. Poster Presentation at Summer Intern Poster Session, Groton Laboratories at Pfizer Research & Development (2024).
• “A Generative AI-based Framework for Toxicity Applications in Early-Stage Drug Development”. Poster Presentation at 9th Annual Biophysics and Structural Biology Research Symposium, Yale University (2024).
• “A Hybrid Quantum-Classical Machine Learning Framework for Drug Toxicity Applications”. Invited Research Talk at QuantumCT Industry Collaboration Forum, Yale West Campus (2024).
• “Generative Machine Learning and Active Learning Methods for Hit Identification in Drug Discovery”. Poster Presentation at Sterling Chemistry Laboratory 101st Anniversary Symposium, Yale University (2024).
• “CardioGenAI: A Machine Learning-based Framework for Re-engineering Drugs for Reduced hERG Liability”. Poster Presentation at 19th Annual Drug Discovery Chemistry Conference (2024).
• “ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation”. Invited Research Talk at Annual Biophysical Society Meeting (2024).
• “Machine Learning and Statistical Methods for Modulating Protein Function with Small Molecule Inhibitors”. Invited Research Talk at National Institutes of Health Biophysics Seminar, Yale University (2023).
• “HAC-Net: A Hybrid Attention-based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction”. Poster Presentation at Annual Biophysical Society Meeting (2023).
• “Introduction to Deep Learning for Chemistry”. Invited Guest Lecture at CHEM 584: Machine Learning and Quantum Computing, Yale University (2022).
• “Photophysics of Binuclear Rhenium(I) Tricarbonyl Complexes and Their Employment as Anion Sensors Through Charge-Mediated Hydrogen Bonding”. Poster Presentation at 261st American Chemical Society National Meeting & Exposition (2021).
• “Variable Anion Recognition Sites in Phosphorescent Rhenium(I) Polypyridyl-based Sensors”. Poster Presentation at 259th American Chemical Society National Meeting & Exposition (2020).
• “Photophysics of Polypyridyl-based Rhenium(I) Complexes and Their Employment as Anion Sensors”. Poster Presentation at 3rd SUNY Binghamton Conference in Chemistry Research (2020).
• “Highly Sensitive Rhenium(I) Sensors for Anions Through Amide Hydrogen Bonding”. Poster Presentation at Undergraduate Research Conference, SUNY Binghamton (2020).
• “Amide Protons as Binding Groups in a Polypyridyl-based Rhenium(I) Anion Sensor”. Poster Presentation at 257th American Chemical Society National Meeting & Exposition (2019).
• “Excited-State Properties of Rhenium(I)-based Anion Sensors”. Poster Presentation at 2nd SUNY Binghamton Conference in Chemistry Research (2019).
• “Organometallic Complexes as Anion Sensors: A Highly Sensitive Rhenium(I) Complex for Cyanide and Halide Anions”. Poster Presentation at 1st SUNY Binghamton Conference in Chemistry Research (2018).

Selected Apps

• BibleVerse
  A modern Bible interface with latent-space verse and passage retrieval, scripture-augmented LLMs designed for breadth- and depth-first knowledge expansion, and a reader with chapter-conditioned LLM chat and annotation.

Scientific Peer Review Contributions

Reviewing Editor

• Springer Nature

Trusted Reviewer

• Nature Communications (Impact Factor: 15.7) | Nature Portfolio
• npj Digital Medicine (Impact Factor: 15.1) | Nature Portfolio
• Environmental Science & Technology (Impact Factor: 11.4) | American Chemical Society
• Journal of Cheminformatics (Impact Factor: 8.6) | BioMed Central
• Engineering Applications of Artificial Intelligence (Impact Factor: 7.5) | Elsevier
• EPJ Quantum Technology (Impact Factor: 5.8) | Springer
• Journal of Chemical Theory and Computation (Impact Factor: 5.7) | American Chemical Society
• Journal of Chemical Information and Modeling (Impact Factor: 5.6) | American Chemical Society
• Computational and Structural Biotechnology Journal (Impact Factor: 4.5) | Elsevier
• Biotechnology (Impact Factor: 4.4) | Oxford University Press
• BMC Bioinformatics (Impact Factor: 3.9) | BioMed Central
• Frontiers in Bioinformatics (Impact Factor: 3.9) | Frontiers
• Scientific Reports (Impact Factor: 3.8) | Nature Portfolio
• Chemical Research in Toxicology (Impact Factor: 3.8) | American Chemical Society
• European Journal of Nuclear Medicine & Molecular Imaging Research (Impact Factor: 3.1) | Springer
• Journal of Computer-Aided Molecular Design (Impact Factor: 3.0) | Springer
• Journal of Molecular Modeling (Impact Factor: 2.1) | Springer
• npj Drug Discovery (Impact Factor: Pending) | Nature Portfolio
• npj Artificial Intelligence (Impact Factor: Pending) | Nature Portfolio