Koes GroupComputational and Systems Biology  ·  School of Medicine  ·  University of Pittsburgh

Removing barriers to computational drug discovery one bit at a time

Koes Group, October 2026
Koes Group · October 2026 · 1 / 2
David Ryan Koes

David Ryan Koes, PhD

I am an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh and Co-Director of the Carnegie Mellon – University of Pittsburgh Ph.D. Program in Computational Biology (CPCB). I am also affiliated with the Computational Biomedicine & Biotechnology (COBB) Master of Science program.

My research combines molecular modeling, algorithm design, and machine learning to make drug discovery better, faster and cheaper. I develop computational algorithms and practical software tools, and apply these methods towards the discovery of new therapeutics. I am committed to open-source software and open science.

Undergraduate/MS Research Application

Prospective PhD Students

Thank you for your interest in our research. I generally try to recruit a new PhD student each year. All PhD positions in the program include funding. Admission is through the graduate program rather than directly into individual labs.

Given that process, I prefer to defer meetings with prospective students until after admission decisions have been made. If your research interests are well-aligned with our group, I encourage you to apply, and I would be happy to discuss potential research opportunities if you are admitted.

Learn more about the Computational Biology PhD program

If you have questions that are not answered above, feel free to email me. I ask that you write in your own words rather than use AI-generated text. A brief to-the-point email is greatly appreciated.

Publications

Google ScholarNCBIPresentations and Data

People

Other Graduate Students

  • Paula Sharoubeem (COBB)
  • Sihyun Park (CMU MSAS)
  • Jiameng Song (COBB)
  • Wenxin Zhang (CMU QuantBio MS)
  • Chenyun Guan (CMU CompBio MS)
  • Jordan Epstein (COBB)
Former Members

Former PhD Students

Former Other Graduate Students

  • Liv Toft (CMU CompBio MS)
  • Justin Shao (CMU CompBio MS)
  • Sumeet Kothare (CMU CompBio MS)
  • Juhi Gupta (CMU CompBio MS)
  • Tyler Katz (CMU CompBio MS)
  • Pratham Lotia (CMU Biotech MS)
  • Yao Wang (COBB)
  • Nate Odonkor (CMU CompBio MS)
  • Andrew Lutsky (CMU QuantBio MS)
  • Minhyek Jeon (CMU CompBio MS)
  • Rosalie Nolen (PittMed)
  • Vratin Srivastava (CMU CompBio MS)
  • Zoey Yang (CMU CompBio MS)
  • Yanjing Li (CMU CompBio MS)
  • William Hsu (CMU CompBio MS)
  • Sitong Liu (CMU CompBio MS)
  • Jackie Vo (CMU CompBio MS)
  • Dillon Gavlock (COBB)

Former Research Fellows and PostDocs

  • Somayeh Pirhadi
  • Dakota Folmsbee
  • Matthew Ragoza
  • Rishal Aggarwal
  • Tomohide Masuda

Teaching

COBB2060: Machine Learning for Biomedical Applications

Successor to Scalable. Less distributed and cloud computing. The focus remains on applications rather than theory. Co-taught with Maria Chikina

Spring Semester (2025–)

Former Courses

MSCBIO2066: Scalable Machine Learning for Big Data Biology

Distributed and cloud computing meets machine learning meets computational biology. The focus is on applications rather than theory. Co-taught with Maria Chikina

Spring Semester (2016–2024)

MSCBIO2025: Introduction to Bioinformatics Programming in Python

A graduate-level introductory programming course with a focus on analyzing biological data.

Fall Semester (2013–2023)

MSCBIO2030: Introduction to Computational Structural Biology

An introduction into the physical, chemical, and algorithmic underpinnings of computational structural biology.

Fall 2022, Spring 2025

CompBio Academy (formerly DiscoBio)

The Computational Biology Summer Academy at UPMC Hillman Cancer Center.

An experiential summer academy for rising high school juniors and seniors.

Co-Director 2013–2023

Software

GitHub →

gnina

Deep learning for molecular docking

pharmit

Interactive exploration of chemical space

3Dmol.js

Molecular visualization with WebGL

OMTRA

Multi-task generative model for structure-based drug discovery.

FlowMol3

Flow matching model for unconditional 3D de novo molecule generation.

libmolgrid

Python library for CUDA accelerated molecular gridding

SolTranNet

Molecule attention transformer for aqueous solubility prediction.

qsar-tools

Collection of scripts for creating and visualizing 2D QSAR models

smina*

Scoring and Minimization with AutoDock Vina

*Developed in collaboration with the Camacho Lab

Funding

I currently have no extramural funding, but a renewal of R35GM140753 is pending.

I have previously received funding from R35GM140753 from the National Institute of General Medical Sciences, CHE-2102474 from the National Science Foundation (with Geoff Hutchison), R21EY032632 from the National Eye Institute (with Partha Roy, Donna Huryn, and Andrew VanDemark), R01GM108340 from the National Institute of General Medical Sciences, CHE-1800435 from the National Science Foundation (with Geoff Hutchison), R21NS107785 from the National Institute of Neurological Disorders and Stroke (with Sam Poloyac and Lee McDermott), Relay Therapeutics, the Samuel and Emma Winters Foundation, the CTSI Biomedical Modeling Pilot Award, and aigrant.org, as well as hardware and software support from NVIDIA and Google Cloud Platform.