# NIH grant: Developing high-throughput free energy methods for small molecule, peptide, and protein design (1R35GM162544-01) — retained record

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NIH grant 1R35GM162544-01: Developing high-throughput free energy methods for small molecule, peptide, and protein design
Institute: {'code': 'GM', 'abbreviation': 'NIGMS', 'name': 'National Institute of General Medical Sciences', 'admin_org_id': '12500024', 'admin_funding_url': ''}. PI: Xiaorong  Liu. Award: $370940.
Start: 2026-08-22T00:00:00.
Project Summary/Abstract
The discovery and design of therapeutic drugs, including small molecules, peptides, and engineered proteins,
are essential for advancing the field of medicine and improving the quality of life. Computational approaches that
leverage fundamental physical principles, as well as available structural and functional data of the drug target,
can enable more efficient usage of experimental resources, thus reducing the cost and time span of the overall
process. Computational tools, such as docking, pharmacophore mapping, and machine learning, can be highly
successful in exploring large chemical and protein sequence spaces to identify a small set of lead molecules that
show promising activities. Moving beyond the initial discovery stage, rigorous physics-based free energy
approaches are often required to more accurately assess and optimize lead molecules. However, a major
bottleneck associated with popular free energy methods is the computational cost and thus limited throughput,
especially when substantial conformational flexibility exists for the receptor and/or the ligand. To address this
bottleneck, we propose to integrate enhanced sampling methods with multisite l dynamics (MSlD) to achieve
an effective computational framework, hereafter referred to as MSlD++, for rigorous high-throughput free energy
predictions in drug discovery and design. MSlD enables the modeling of multiple substituents at multiple sites
of a common ligand core and can yield free energies for thousands of molecules in a single simulation. It is thus
particularly powerful for solving the combinatorial complexity in the exploration of chemical and protein sequence
space frequently involved in lead optimization. Furthermore, this unique feature of MSlD allows one to run much
longer simulations and/or incorporate more sophisticated simulation techniques to adequately sample protein
and ligand dynamics for accurate high-throughput free energy calculations, even for challenging cases involving
various levels of structural flexibility. The proposed MSlD++ development will be coupled with three important
classes of applications with an increasing level of complexity and demand for conformational sampling: (1)
Efficient sampling of protein local structural changes in small molecule drug discovery; 2) Design of potent and
selective therapeutic peptides; (3) Understanding and engineering transcription factors for DNA recognition.
Successful development of MSlD++ algorithms will push the limit of accuracy and throughput for free energy-
based approaches in the rational design of small-molecule drugs, therapeutic peptides, and functional proteins.
Throughout the project, we will engage in close experimental collaborations to critically evaluate the
effectiveness of MSlD++, thus allowing us to deliver robust tools to the broad scientific community.
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