Research Interests

Our research program focuses upon the application and development of new computational tools that target organic and enzymatic catalyst design, alternative environmentally friendly solvent design, and drug discovery. Fundamental problems in organic and medicinal chemistry are probed, such as elucidation of enzymatic reactions, controlling enantioselectivity for chiral compounds, transition structure prediction, de novo design of high-affinity inhibitors, and origins of drug resistance. Obtaining quantitative success with large-scale quantum and molecular mechanical calculations involves the development of improved force fields, machine learning software, and methodology.

Machine Learning

Nvidia CUDA MCGPU Background: Our aim is to develop and apply machine learning tools (i.e., artificial intelligence) to provide deeper insight into the relationship between intermolecular interactions and macroscopic measurements. Our specific interests have us developing machine learning (genetic algorithm) software to automate force field parameterization, training machine learning (neural networks) tools to replicate solvent environments, and applying machine learning to study chemical reactivity in solution.

Objective: We are developing an open source program called Genetic Algorithm Machine Learning (GAML) that applies machine learning, i.e., genetic algorithm (GA), to automate solvent parameterization. The most current build is available on our Github page. In addition, we are developing machine learning potentials, i.e., Artificial Neural Networks (ANN), that compute energies with quantum mechanical accuracy at the blistering speed of a nonpolarizable force field. Our current interests lie in studying unique solvent environments, e.g., ionic liquids and deep eutectic solvents, and applying machine learning to biological systems for drug discovery and catalysis.

Download software and force field parameters on our Github page:

Drug Discovery

CypA/B with inhibitor Background: The Acevedo group collaborates with experimentalists across the country with the goal of developing inhibitors for the treatment of multiple diseases. Varied techniques are applied including: free energy perturbations, docking, molecular dynamics, Monte Carlo, and ADME predictions.

Collaborations with experimentalists include:
(1) Prof. Venkatesan Jayaprakash (School of Pharmacy, Birla Institute of Technology, India) - Development of MAO-A and MAO-B selective inhibitors exhibiting anti-depression properties.
(2) Prof. Raj Amin (School of Pharmacy, Auburn University) - Development of partial agonists for PPAR-γ and PPAR-δ for anti-diabetic and anti-alzheimer's properties.
(3) Prof. Bhabatosh Chaudhuri (School of Pharmacy, De Montfort University, UK) - Development of CYP1A1 and CYP1B1 inhibitors exhibiting anti-cancer properties.

Solvent Effects and Catalysis

Ketosteroid Isomerase with steroid Background: The aim is to develop and use computational methods to gain physical insight into how external factors (enzymes or solvent) enhance the rate or stereoselectivity of chemical reactions. For many reactions the role of solvent has been assumed to be static, hence its effect is basically thought to be a contribution of solvation energy to the total free energy of the system. However, direct participation of solvent molecules may occur in which a few critical solvent molecules bind to the transition structure and lower the activation energy or an electric field created by the solvent changes the shape of the potential energy surface. In extreme cases the reaction path itself can be perturbed, especially when Lewis acids are involved. This reinforces the need for thorough studies on the intermolecular interactions occurring between solvents, catalysts, and reactions.

Collaborations with experimentalists include:
(1) Prof. Holly Ellis (Auburn University) - Mechanism elucidation for flavin-dependent monooxygenase enzymes.
(2) Prof. Joan Hevel (Utah State University) - Mechanism elucidation for protein arginine methylation (PRMT1).

Ionic Liquids

Dehydrobromination reaction in ionic liquid Background: Ionic liquids are a novel class of solvents, defined as a material containing only ionic species, with a melting point at or below room temperature. In sharp contrast to molten salts or melts, ionic liquids can be fluid at temperatures as low as 204 K, are colorless, have low viscosities, high conductivity, negligible vapor pressure, excellent thermal and chemical stabilities, are recyclable, non-explosive, easy to prepare, active at room temperature, and tolerate impurities such as water. An exciting aspect of ionic liquids resides in their ability to provide increased rates and selectivity for a series of industrially and academically important reactions such as the Heck, Friedel-Crafts, isomerizations, hydrogenation, organometallic, Michael, Mannich, Wittig, 1,3-Dipolar additions, aldol and benzoin condensations. The observed effects of ionic liquids range from weak to powerful, but an understanding of the molecular factors are largely unknown.

Objective: The objective is to understand the microscopic details on how ionic liquids operate and to exploit this understanding to predict new ionic liquids that give optimal rate and stereoselectivity enhancements. A comprehensive understanding on how ionic liquids impact chemical reactivity will be used to influence a wide range of (1) difficult organic reactions, which benefit from toxic solvents coupled with high pressures and temperatures, and (2) enzymatic reactions, which require complex physiological conditions. The long-term intent of our research program is to create controllable, efficient, safe, and environmentally clean technologies that impact society and chemistry from the laboratory bench top to large-scale industrial manufacturing.


National Science Foundation • NSF, National Science Foundation (current):
PI: CHE-2102038 - Machine Learning
PI: CHE-2003615 - PRMT
Co-PI: CHE-1808495 - Monooxygenase
Co-investigator: (CNS-1949972) - REU Site

• NSF, National Science Foundation (past):
Co-investigator: (CNS-1659144) - REU Site
PI: CHE-1562205 (CHE-1464918) - Ionic Fluids/Software Development
PI: CHE-1626860 (CHE-1412358) - PRMT1
PI: CHE-1561010 (CHE-1149604) - Ionic Liquids
Co-investigator: DEB-1244320 - Monooxygenase

• Institute for Data Science & Computing, University of Miami