# Computational alchemy for molecular design and optimization

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2022 · $334,546

## Abstract

PROJECT SUMMARY/ABSTRACT
Pharmaceutical drug discovery is time-consuming and expensive, with each new drug brought to market now
costing well over $1 billion on average. This cost is driven by the difficulty of drug discovery, and in part by the
amount of trial and error involved in the process of finding initial “hits” which modulate the function of a biomolecule,
and then refining these into “leads” which have adequate affinity for the biomolecular target and other desirable
properties. Here, we develop and improve computational methods to guide this process, allowing the potential
efficacy of prospective leads to be tested computationally prior to their creation — dramatically reducing the
amount of trial and error involved in the process and guiding the molecular design process.
Here, we build on previous work in the group and the field on alchemical free energy calculations based on
molecular simulations — the most promising present computational technique for guiding drug discovery. However,
such techniques work well only for a limited subset of cases, require considerable expertise to employ, and even
their limitations are not yet well understood. Here, we focus on expanding the range of systems which can be
treated with these techniques, making the calculations more robust and rapid, improving accuracy, and identifying
and isolating remaining deficiencies for repair.
Alchemical free energy calculations hold particular promise both because of their accuracy and physical real-
ism. Here, we focus on technology and applications of these calculations, focusing on (1) improved efficiency
and accuracy of binding free energy calculations; (2) automation and large-scale benchmarking of free energy
calculations to guide work to ensure robustness and accuracy; and (3) applications to utilizing simulations and
free energy calculations to guide lead discovery and optimization of SUMO E-1 inhibitors as potential anti-cancer
drugs. Broadly, Aims 1-2 focus on iteratively improving and testing computational tools, whereas Aim 3 focuses
on a specific application with experimental collaborators.
This work promises more accurate and more rapid free energy calculations, with broader scope so that they can
reliably be applied to molecular design problems in drug discovery and elsewhere. Our long-term work aims to
produce a workflow where a chemist developing new molecules to bind a particular target could input hundreds
of potential compounds to synthesize next into a computer before leaving work one day, and return to work the
following morning to find these compounds automatically prioritized based on predicted target affinity, selectivity,
solubility and other properties, allowing years worth of synthesis and assays to be bypassed. Here, we develop,
test and apply technologies to help make this workflow possible, building on our extensive previous success in
physical modeling for binding prediction. This also leverages and extends technologies built in our prior R01.

## Key facts

- **NIH application ID:** 10472624
- **Project number:** 5R01GM108889-08
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** David Lowell Mobley
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $334,546
- **Award type:** 5
- **Project period:** 2014-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10472624

## Citation

> US National Institutes of Health, RePORTER application 10472624, Computational alchemy for molecular design and optimization (5R01GM108889-08). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10472624. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*