# Deep learning based antibody design using high-throughput affinity testing of synthetic sequences

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $591,130

## Abstract

Project Summary
We will develop and apply a new high-throughput methodology for rapidly
designing and testing antibodies for a myriad of purposes, including cancer and
infectious disease immunotherapeutics. We will improve upon current
approaches for antibody design by providing time, cost, and humane benefits
over immunized animal methods and greatly improving the power of present
synthetic methods that use randomized designs. To accomplish this, we will
display millions of computationally designed antibody sequences using recently
available technology, test the displayed antibodies in a high-throughput format at
low cost, and use the resulting test data to train molecular dynamics and
machine learning methods to generate new sequences for testing. Based on our
test data our computational method will identify sequences that have ideal
properties for target binding and therapeutic efficacy. We will accomplish these
goals with three specific aims. We will develop a new approach to integrated
molecular dynamics and machine learning using control targets and known
receptor sequences to refine our methods for receptor generalization and model
updating from observed data (Aim 1). We will design an iterative framework
intended to enable identification of highly effective antibodies within a minimal
number of experiments, in which our methods automatically propose promising
antibody sequences to profile in subsequent assays (Aim 2). We will employ
rounds of automated synthetic design, affinity test, and model improvement to
produce highly target-specific antibodies. (Aim 3).
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## Key facts

- **NIH application ID:** 9878070
- **Project number:** 5R01CA218094-03
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** David K Gifford
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $591,130
- **Award type:** 5
- **Project period:** 2018-03-09 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878070

## Citation

> US National Institutes of Health, RePORTER application 9878070, Deep learning based antibody design using high-throughput affinity testing of synthetic sequences (5R01CA218094-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9878070. Licensed CC0.

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