Pin-Kuang Lai
Assistant Professor
Charles V. Schaefer, Jr. School of Engineering and Science
Department of Chemical Engineering and Materials Science
Education
- PhD (2018) University of Minnesota (Chemical Engineering)
Research
My research is dedicated to tackling the challenges of drug design and development by using machine learning, molecular simulations and high-throughput screening. In particular, our lab focuses on antimicrobial peptides (AMPs) and antibodies.
The emergence of antimicrobial resistance is a crucial public health problem due to the dissemination of bacterial strains that are resistant to multiple antibiotic drugs. AMPs are promising alternatives to traditional antibiotics. One of the most desirable advantages of AMPs is that bacterial resistance would evolve much more slowly than against antibiotics. However, AMPs can exhibit undesirable properties as drugs, including short circulating half-life, instability and toxicity to animals and humans. Therefore, novel approaches are needed to be developed to make AMPs less toxic for human while maintaining or improving their potency to eliminate bacteria and reduce the production cost.
Monoclonal antibodies (mAbs) have been used as therapeutic drugs for over 30 years. One of the outstanding issues of antibody drug is the poor stability of some drug candidates such as high aggregation, elevated viscosity and low solubility. This hinders the development of new antibodies. The production cost of antibodies is high, therefore developing computational tools that can predict antibody stability in the early-stage discovery is desired.
The emergence of antimicrobial resistance is a crucial public health problem due to the dissemination of bacterial strains that are resistant to multiple antibiotic drugs. AMPs are promising alternatives to traditional antibiotics. One of the most desirable advantages of AMPs is that bacterial resistance would evolve much more slowly than against antibiotics. However, AMPs can exhibit undesirable properties as drugs, including short circulating half-life, instability and toxicity to animals and humans. Therefore, novel approaches are needed to be developed to make AMPs less toxic for human while maintaining or improving their potency to eliminate bacteria and reduce the production cost.
Monoclonal antibodies (mAbs) have been used as therapeutic drugs for over 30 years. One of the outstanding issues of antibody drug is the poor stability of some drug candidates such as high aggregation, elevated viscosity and low solubility. This hinders the development of new antibodies. The production cost of antibodies is high, therefore developing computational tools that can predict antibody stability in the early-stage discovery is desired.
General Information
Our group is dedicated to tackling the challenges of biomanufacturing and biotherapeutics development by using integrative machine learning, computer simulations, metabolic engineering, and experimental screening. Our lab focuses on the areas of antibody drug developability, formulation design, and metabolic flux analysis.
Experience
Postdoc: Chemical Engineering, MIT (2018-2021)
Institutional Service
- Research computing committee Member
- Center for Healthcare Innovation Member
- Stevens Institute for Artificial Intelligence Member
- Graduate Research Committee Member
- Graduate Study Committee Member
- Working Group on Core AI Grad Curriculum Member
- High School Visit Committee Member
- Department seminar coordinator Chair
- SES PhD virtual Open House Member
- Faculty Search Committee Member
Professional Service
- mAbs Journal Editor
- Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) Reviewer
- National Institute for Innovation in Manufacturing Biopharmaceuticals Governing Committee
- AIM-AHEAD Mentor
- AstraZeneca Consultant
- ZGM1 BBCB-A (SB) study section, NIH Reviewer
- ACS Spring 2025 Area coordinator
- Proteinea Consultant
- Innovations in Nanosystems and Nanotechnology (INN) study section, NIH Reviewer
- ACS Spring 2024 Area coordinator
- ACS Fall 2023 Area coordinator
Consulting Service
AstraZeneca
Appointments
Assistant Professor, Chemical Engineering and Materials Science, Stevens Institute of Technology (2021-Present)
Innovation and Entrepreneurship
N/A
Honors and Awards
Keynote speakers:
-- AAPS NBC 2025
-- Bioprocessing summit 2024
-- AAPS NBC 2025
-- Bioprocessing summit 2024
Professional Societies
- ACS – American Chemical Society Member
- AICHE – American Institute of Chemical Engineers Member
- AAPS – American Association of Pharmaceutical Scientists Member
Grants, Contracts and Funds
Title: Next Generation Viscosity Prediction for Molecular Liability Reduction and Multi-parameter Optimization Development
Award Number: AGMTX 20250414
Source: Janssen Research & Development
Role: PI
Award Amount: $150,000
Date: 04/2025-04/2026
Title: Hybrid data-driven and physics-based models for rapid prediction of antibody-antibody and antibody-excipient interactions
Award Number: UDR0000708
Source: University of Delaware
Role: PI
Award Amount: $90,000
Date: 01/2025-12/2027
Title: Whole-body-level metabolic flux quantitation by machine learning
Award Number: 1R21AT012694-01
Source: National Institute of Health
Role: PI
Award Amount: $191,321
Date: 07/2024-04/2026
Title: Toward Metagenome-Scale Metabolic Flux and Free Energy Analysis via Deep Learning
Award Number: DE-SC0024251
Source: Department of Energy
Role: PI
Award Amount: $525,000
Date: 09/2023-08/2026
Title: Applying molecular dynamics simulations and machine learning to predict antibody-drug developability and bioavailability
Award Number: UDR0000708
Source: University of Delaware
Role: PI
Award Amount: $90,000
Date: 10/2022-10/2024
Award Number: AGMTX 20250414
Source: Janssen Research & Development
Role: PI
Award Amount: $150,000
Date: 04/2025-04/2026
Title: Hybrid data-driven and physics-based models for rapid prediction of antibody-antibody and antibody-excipient interactions
Award Number: UDR0000708
Source: University of Delaware
Role: PI
Award Amount: $90,000
Date: 01/2025-12/2027
Title: Whole-body-level metabolic flux quantitation by machine learning
Award Number: 1R21AT012694-01
Source: National Institute of Health
Role: PI
Award Amount: $191,321
Date: 07/2024-04/2026
Title: Toward Metagenome-Scale Metabolic Flux and Free Energy Analysis via Deep Learning
Award Number: DE-SC0024251
Source: Department of Energy
Role: PI
Award Amount: $525,000
Date: 09/2023-08/2026
Title: Applying molecular dynamics simulations and machine learning to predict antibody-drug developability and bioavailability
Award Number: UDR0000708
Source: University of Delaware
Role: PI
Award Amount: $90,000
Date: 10/2022-10/2024
Patents and Inventions
N/A
Selected Publications
Book Chapter
Journal Article
- Lai, P. (2025). Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning. mAbs (1 ed., vol. 17, pp. e2483944).
- Lai, P. (2025). Kinetic analysis of low-barrier nucleation via first-passage time distributions: A CO2 hydrate case study. J. Mol. Liq (vol. 418, pp. 126702).
- Lai, P. (2025). Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-based Surface Descriptors. Mol. Pharm (1 ed., vol. 22, pp. 142-153).
- Lai, P. (2024). Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning (vol. 23, pp. 3669–3679). Comput. Struct. Biotechnol. J.
- Harrison, M. C.; Lai, P. (2024). Investigating the mechanisms of antibody binding to alpha-synuclein for the treatment of Parkinsons Disease. Molecular Pharmaceutics. Washington, D.C.: American Chemical Society.
- Kalejaye, L.; Wu, I.; Lai, P. (2024). DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability. Computational and Structural Biotechnology Journal (vol. 23, pp. 2220-2229). Amsterdam: Elsevier.
https://www.sciencedirect.com/science/article/pii/S2001037024001739. - Phillips, A.; Srinivas, A.; Prentoska, I.; O'Dea, M.; Kustrup, M.; Hurley, S.; Bruno, S.; Nguyen, V.; Lai, P. (2024). Teaching biologics design using molecular modeling and simulations. Biochemistry and Molecular Biology Education (3 ed., vol. 52, pp. 299-310). Edison, NJ: John Wiley & Sons, Inc..
https://iubmb.onlinelibrary.wiley.com/doi/full/10.1002/bmb.21813. - Lai, P.; Phillips, A.; Srinivas, A.; Prentoska, I.; O'Dea , M.; Kustrup, M.; Hurley, S.; Bruno, S.; Nguyen , V. (2023). Teaching biologics formulation using molecular modeling and simulations. ChemRxiv.
https://chemrxiv.org/engage/chemrxiv/article-details/647b9c2e4f8b1884b7c95317. - Lai, P. (2022). DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity. Computational and Structural Biotechnology Journal (vol. 20, pp. 2143-2152). Elsevier.
https://www.sciencedirect.com/science/article/pii/S2001037022001520?via%3Dihub. - Lai, P. (2022). Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics. mAbs (1 ed., vol. 14, pp. e2026208). Taylor & Francis.
https://www.tandfonline.com/doi/full/10.1080/19420862.2022.2026208.
Courses
CHE542: Data Science in Pharmaceutical Development
CHE633: Advanced Heat and Mass Transfer
CHE351: Reactor Design
CHE630: Theory of Transport Process
CHE700: Seminar in Chemical Engineering
CHE633: Advanced Heat and Mass Transfer
CHE351: Reactor Design
CHE630: Theory of Transport Process
CHE700: Seminar in Chemical Engineering