# Combining structure-based network biology and heterogeneous computing for rational drug repositioning and polypharmacology

> **NIH NIH R35** · LOUISIANA STATE UNIV A&M COL BATON ROUGE · 2020 · $192,411

## Abstract

1. Project Summary
Drugs are typically developed to modulate the function of specific proteins, which are directly associated with
particular disease states. Nonetheless, recent studies suggest that protein-drug interactions are promiscuous
and the majority of pharmaceuticals exhibit activity against multiple, often unrelated proteins. The lack of
selectivity often leads to undesired drug side effects; yet, these polypharmacological attributes can be used to
develop drugs that act on multiple targets of a unique disease pathway, as well as to identify new targets for
existing drugs, known as drug repositioning. Although predicting interactomes is becoming increasingly
important in drug discovery, a large number of interacting molecules and highly complicated interaction
patterns present significant challenges. Clearly, novel computational approaches are desperately needed to
rigorously explore drug cross-reactivity. The overall goal of the proposed research is, therefore, to combine a
broad scope and promises of computational systems biology, atomic-level modeling of medically relevant
biomolecules and interactions among them, and heterogeneous computing using massively parallel
accelerators to study drug-oriented interactomes. This innovative project comprises several components. First
is to design a fully automated platform for structure-based ligand virtual screening featuring an information
theory-based compound selection. By using the Maximum Entropy Method, we will be able to enhance the
specificity of scoring functions for ligand ranking. Second, we plan to improve the across-proteome
identification of chemically similar drug binding pockets by combining local binding site alignment with
molecular docking. The advantage of this new strategy is the capability to explore a much larger space of
putative cross-interactions between proteins and small organic compounds. Third, we are going to use new
modeling techniques described above to reconstruct and investigate protein-drug interaction networks in the
human proteome. By developing novel multi-target antibiotics, we will demonstrate that the proposed network
analysis greatly expands the current opportunity space for polypharmacology and rational drug repositioning.
Fourth, the scale of the task at hand as well as the level of details put an unprecedented demand for
computing resources. Consequently, there is an urgent need to take advantage of modern computer
architectures currently available as well as exascale supercomputers that are expected to come into production
in the near future. On that account, we plan to develop high-performance codes to fully utilize heterogeneous
machines equipped with massively parallel hardware accelerators, NVIDIA GPU and Intel Xeon Phi. Close
collaborations with experimental and computer science groups will be part of the proposed research to make
advances in this highly specialized field. The expected overall impact of this innovative proposal is that it wi...

## Key facts

- **NIH application ID:** 9984412
- **Project number:** 5R35GM119524-05
- **Recipient organization:** LOUISIANA STATE UNIV A&M COL BATON ROUGE
- **Principal Investigator:** Michal Brylinski
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $192,411
- **Award type:** 5
- **Project period:** 2016-07-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984412

## Citation

> US National Institutes of Health, RePORTER application 9984412, Combining structure-based network biology and heterogeneous computing for rational drug repositioning and polypharmacology (5R35GM119524-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9984412. Licensed CC0.

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