Randolph M. Lopez

The Synthetic Epitope Atlas: High-Throughput Design and Validation of De Novo Antibody-Antigen Complexes

The paper generates and validates synthetic epitope proteins to improve de novo antibody design by combining computational design with high-throughput experimental binding data.

Effects of protein interface mutations on protein quality and affinity

The paper disentangles protein-interaction and protein-quality effects in antibody-antigen binding using deep mutational scanning and evaluates models for their ability to predict these effects.

AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity

AlphaBind predicts and optimizes antibody-antigen binding affinity using a domain-specific model trained on quantitative binding data.

High-throughput ML-guided design of diverse single-domain antibodies against SARS-CoV-2

This study uses ML to design diverse VHH antibodies that neutralize SARS-CoV-1 and SARS-CoV-2 variants, including Delta and Omicron BA.1.

Machine Learning Optimization of Candidate Antibodies Yields Highly Diverse Sub-nanomolar Affinity Antibody Libraries

This paper uses a Bayesian language model to design high-affinity scFv antibody libraries, achieving 28.8-fold better binding than directed evolution.

Experimental Assessment of PCR Specificity and Copy Number for Reliable Data Retrieval in DNA Storage

The paper demonstrates reliable PCR-based data retrieval from a complex DNA storage pool with high density and varying file sizes.