About

Dr. Gregory W. Kyro is an AI Scientist at Lila Sciences, where he designs and executes core machine learning research efforts to advance Lila's mission of building Scientific Superintelligence. He earned his PhD in Computational Biophysical Chemistry from Yale University in three years as an NSF Fellow, where he developed deep learning models that advanced the state of the art in drug discovery and were implemented in collaboration with leading companies including Pfizer, NVIDIA, Novartis, Moderna, Merck Group, and others.

Some noteworthy developments that Gregory created during his PhD are:

• HAC-Net: state-of-the-art deep learning model for predicting protein-ligand binding affinity—used to identify a potential inhibitor of a G protein-coupled receptor whose overexpression leads to cancer, diabetes, and multiple sclerosis, as well as a potential antivirulence drug for drug-resistant staphylococcal infections
• CardioGenAI: generative deep learning framework for re-engineering drugs for reduced hERG-related cardiotoxicity while preserving their primary pharmacology—applied to specific programs within Pfizer R&D that were dealing with hERG liabilities; subsequently generalized to additional toxicity endpoints and served as the foundational framework for a project that was awarded first place in the Blavatnik Fund for Innovation at Yale Ventures, resulting in seed funding toward a startup now in development
• T-ALPHA: state-of-the-art deep learning model for predicting protein-ligand binding affinity—used to identify potent small-molecule binders to the R1R2 interface of talin, thereby mitigating thoracic aortic aneurysm, a vascular disease driven by impaired cellular stiffness sensing
• ChemSpaceAL: first active learning methodology for fine-tuning a molecular generative model toward a specified protein target—utilized to design small-molecule binders to the HNH domain of CRISPR-Cas9 to enhance its specificity for target DNA sequences

He also developed Electrostatic Eigenvector Centrality—a graph-based information-theoretic method for describing intra-protein information transfer—which was used to characterize the allosteric mechanisms of CRISPR-Cas9, IGPS, and MIF-2, all of which were experimentally validated.

During his PhD, Gregory served as one of the founding scientists of QuantumCT—a Yale-affiliated initiative aimed at accelerating quantum research partnerships between public and private sectors—where he led the foundational scientific efforts, established collaborations with numerous industry partners including Pfizer, NVIDIA, Novartis, and Moderna, and published the initiative’s first collaborative paper as first author—efforts that ultimately catalyzed a $10M investment from the Connecticut State government.

Moreover, Gregory co-developed an AI-based rapid synthesis framework that was acquired by Merck Group and is currently being integrated across multiple teams within the company. He also contributed to the fine-tuning of OpenAI’s large language models, developed software for PROTACs screening at OpenEye Scientific, and developed quantum computing-based methods in collaboration with NVIDIA and Moderna.

Gregory founded and served as President of the Yale University Chapter of the Biophysical Society, published numerous papers in top-tier academic journals, won first place at highly selective and competitive competitions, presented his work at several conferences, created multiple Python packages, and established various collaborations with labs around the world. For these reasons, he has received numerous highly prestigious awards and been featured across major scientific and industry media outlets.