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David Pearlman

Vice President of Product at QSimulate

David Pearlman is a pioneering computational chemist known for his primary contributions to major molecular modeling platforms like AMBER, Schrodinger, and Discovery Studio.

With an extensive background in computational chemistry, protein and antibody design, software program management, and structure-based drug design, David has a proven track record of success in the industry.

His milestones include being the first author of multiple versions of Amber, a widely used molecular modeling package, and developing the successful BioLuminate biologics structure design platform for Schrödinger.

David's communication skills, technical expertise, and ability to manage multiple clients make him a valuable asset in the field.

He holds a B.S. in Chemistry from Stanford University, a Ph.D. in Chemistry from the University of California, Berkeley, and has completed a Post Doc in Computational Chemistry at the University of California, San Francisco.

Throughout his career, David has held key positions at organizations like QSimulate, Cyrus Biotechnology, Schrodinger, Vertex Pharmaceuticals, and Biosym Technologies.

His contributions to methodology development, free energy calculations, NMR refinement methods, and molecular dynamics have significantly impacted the industry.

In addition to his work in computational chemistry, David was the first person to provide a formal avenue for music promotion on the Internet.

With a focus on computational chemistry, molecular modeling, and protein design, David Pearlman continues to be a leading figure in the field, combining his technical expertise with exceptional writing and verbal skills.

Highlights

Jun 27 · linkedin

Pi-stacking interactions in drug discovery: Undeniably important but MM FFs are severely limited. QM/MM is your friend.

While we can finesse a few commonly found canonical parallel pi-stacking geometries in classical force fields, once you move outside that domain (or start to add unexpected ring substitutions), things get dicey. Attempting to get actionable predictions using classical force fields for compounds like these can be an exercise in futility. Understanding these interactions is far better suited to quantum mechanics (see, e.g., https://lnkd.in/ezYUh3j2 ) no one could afford to integrate QM into things like MD/FEP with sufficient turnaround for pharma. Until now, at least. Now it's possible (See QSimulate)

This is increasingly going to be the road forward.

Attached is a nice blog post from today by Gilles Ouvry that provides some examples of where T-shaped aromatic interactions are hugely significant to ligand binding. Anyone who has worked in med chem knows these are just the tip of the iceberg...

#computationalchemistry #quantummechanics #drugdiscovery #forcefields #moleculardynamics #freeenergycalculations #medicinalchemistry

Jun 19 · linkedin

Halogens: Hugely important for drug discovery but poorly described in classical FFs. Quantum mechanics now fills the gap.

A new article in JPC by Leon Wehrhan and Bettina G. Keller discusses the importance of Fluorine in drug discovery (about 20% of all registered pharmaceuticals contain Fluorine), and the continuing shortfalls of all classical force fields when properly modeling fluorine (and all halogens). These shortfalls include the well-known difficulty in modeling the "sigma hole," as well as properly addressing hydrogen bonding to halogens.

The straightforward solution is to use quantum mechanics to describe the halogenated ligand and surroundings. That hasn't been computationally tractable with pharma-relevant turnaround in the past for things like MD and FEP, but we've figured out how to do it at QSimulate.

#computationalchemistry #molecularmodeling #moleculardynamics #forcefields #freeenergycalculations #fep #quantummechanics

David Pearlman
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Location

Lexington, Massachusetts, United States