# Tackling Multifaceted Drug Design Problems with Lambda Dynamics Based Technologies

> **NIH NIH R35** · INDIANA UNIVERSITY INDIANAPOLIS · 2022 · $383,323

## Abstract

Project Summary
Modern day drug discovery is a long and expensive process requiring teams of scientists, multiple years of
research, and millions of dollars to identify preclinical drug candidates suitable for clinical tests. The incorporation
of computational tools into drug discovery has proved an effective means to reduce these costs. All-atom
molecular dynamics simulations coupled with alchemical free energy calculations have been extremely beneficial
tools for studying structural and thermodynamic properties of protein-ligand complexes and optimizing drug
candidates for improved binding affinity to a target of interest. Lambda dynamics (LD), a newer alchemical free
energy method, facilitates the sampling of multiple perturbations to a chemical system, simultaneously, within a
single molecular dynamics simulation, overcoming inherent scalability limitations associated with conventional
free energy methods. To date, a variety of chemical perturbations, including diverse ligand functional group
transformations and protein side chain mutations, have been performed with (LD) on a single chemical entity,
e.g., a small molecule or protein, with much success. Tens to hundreds of chemical states have been efficiently
sampled using an order of magnitude less computational resources compared to conventional methods. This
proposal seeks support to build upon these findings and apply LD-based techniques to explore multifaceted
design problems in drug discovery featuring chemical modifications on multiple binding partners. Specifically,
three challenging areas of drug discovery will be investigated: (1) understanding and overcoming drug resistance
originating from missense mutations in a drug target, (2) characterizing protein-protein interactions and binding
specificities, and (3) automating the generation of novel, target-specific lead compound analogs by integrating
LD calculations with machine- or deep-learning algorithms. Success in these efforts will require searching
through large combinatorial chemical spaces that can only be accomplished with LD-based techniques. Model
protein-target systems of high therapeutic importance from Multiple Myeloma or Alzheimer’s Disease will be
investigated in accomplishing our goals. Thus, this work will assist in accelerating preclinical structure-based
drug design by enabling complex molecular design scenarios to be addressed in these devastating diseases.

## Key facts

- **NIH application ID:** 10499372
- **Project number:** 1R35GM146888-01
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** JONAH VILSECK
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $383,323
- **Award type:** 1
- **Project period:** 2022-09-24 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10499372

## Citation

> US National Institutes of Health, RePORTER application 10499372, Tackling Multifaceted Drug Design Problems with Lambda Dynamics Based Technologies (1R35GM146888-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10499372. Licensed CC0.

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