# Structure based Prediction of the interactome

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $354,744

## Abstract

The interactions of small molecules with proteins is not only omnipresent throughout cellular processes, but
also of fundamental importance to drug design and disease treatment. Much like with protein-protein and
protein-RNA interactions, high-throughput (HTP) experimental methods have led to the generation of
enormous volumes of protein-small molecule and related data. However, in addition to sheer scale, high
degrees of heterogeneity in these data, combined with proprietary ownership concerns when originating from
within industry, present significant challenges to the sharing and full use of this data in basic research and
therapeutic development. This proposal aims to develop new mathematical methods that can address not only
interpreting the data itself, but also the collaborative and generative process through which researchers work:
new cryptographic tools can enable unprecedented forms of secure sharing and collaboration between industry
and the public, and deep learning of structural features can reduce the dependence of researchers on prior
assumptions as to important predictors of drug-target interactions (DTI).

In our previous granting period, we successfully developed methods for structure-based prediction and HTP
data analysis of protein-protein and protein-RNA interactions, uncovering novel biology (e.g., for
neurodegenerative diseases). In this renewal, we aim to: 1) develop scalable methods for multi-party
computation and differential privacy to enable the secure sharing of large proprietary drug-target interaction
databases among industry and public researchers; 2) develop novel integrative machine learning approaches
for identifying drug-target interactions based on interactome, molecular structure and chemogenomic data (in
collaboration with co-I Jian Peng); and 3) establish innovative collaborations with industry, academia and the
scientific community to drive use and adoption of these computational tools and technologies among practicing
biomedical researchers.

Successful completion of these aims will provide both public and private research communities with scalable
access to technologies for secure sharing of proprietary drug screening data as well as flexible, accurate tools
for predicting drug-target interactions. All developed software will be made available via publicly accessible
web-based portals under open source software licenses. Collaborations with research partners will validate the
relevance of these tools to human health and disease, while the dissemination aim will ensure research
communities convenient and ongoing access to these innovations.

## Key facts

- **NIH application ID:** 9935079
- **Project number:** 5R01GM081871-12
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** BONNIE BERGER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $354,744
- **Award type:** 5
- **Project period:** 2008-04-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9935079

## Citation

> US National Institutes of Health, RePORTER application 9935079, Structure based Prediction of the interactome (5R01GM081871-12). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9935079. Licensed CC0.

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