NEWS
NEWS
Embedded Files
Dr. Lai will deliver a keynote on "AI in Antibody Discovery and Development" at 2025 Taiwanese American Association of Biotechnology Symposium on Nov 22.
https://www.linkedin.com/feed/update/urn:li:activity:7377158792624402432/
Dariya received £10,000 Apoha Discovery Grant to support her Ph.D. research on formulation design of monoclonal antibody therapeutics. The project titled “Viscosity Prediction of High-Concentration Monoclonal Antibodies: Integrating Physico-Chemical Liquid Brain® Embeddings with Deep Learning” investigates how Apoha’s high-throughput Liquid Brain® assay can strengthen machine learning (ML) models for predicting antibody viscosity — a critical challenge in developing high-concentration formulations for subcutaneous drug delivery.
Onyedika received a $10,000 scholarship from Nokia Bell Labs. His work develops computational frameworks that combine metabolic modeling, multi-isotope tracing, and machine learning to analyze complex cell interactions. This approach enhances cancer research, metabolic engineering, and AI-guided discovery for sustainable chemical production and smart biomaterials.
Dr. Lai is named a 2024 Top Scholar by ScholarGPS in the specialty of Antibody Research (Prior 5 Years)!
The Lai group received funding titled "Next Generation Viscosity Prediction for Molecular Liability Reduction and Multi-parameter Optimization Development," $150,000, Janssen Research & Development.
Dr. Lai delivered a keynote presentation at AAPS NBC on predicting high-concentration antibody developability using machine learning.
Congrats, Lateefat, on publishing the DeepViscosity paper, our latest machine learning model for predicting high-concentration antibody viscosity.
The Lai group received funding titled "Hybrid data-driven and physics-based models for rapid prediction of antibody-antibody and antibody-excipient interactions," $90,000, University of Delaware/BITC.
Congrats, Ibrahim, on publishing his first paper as a high school student! Ibrahim developed machine learning models that can rapidly screen potential drugs targeting the TG2 protein for treating celiac diseases.
The Lai group received funding titled "Whole-body-level metabolic flux quantitation by machine learning," $191,321, NIH. This research is in collaboration with UCLA.
Stevens officially joins NIIMBL (https://www.niimbl.org/).
Dr. Lai will be the leading PI at Stevens.
The Lai group received funding titled "Toward Metagenome-Scale Metabolic Flux and Free-Energy Analysis via Deep Learning," $525K, DOE. This award is part of a $1.05M collaborative research grant with UCLA.
Malcolm Harrison, REU from County College of Morris won first place in the poster competition.
The Lai group received funding titled "Applying molecular dynamics simulations and machine learning to predict antibody-drug developability and bioavailability," $90,000, University of Delaware/BITC.
Page updated
Report abuse