Alex C. Morehead

Ph.D. Candidate in Machine Learning & Computational Biology @Mizzou

About

Welcome! :wave:

I am a Computer Science Ph.D. candidate at the University of Missouri. I work as a graduate research assistant in Dr. Jianlin Cheng’s Bioinformatics & Machine Learning Lab. Prior to my Ph.D., I completed my B.S. in Computer Science at Missouri Western State University. My interests in computer science and machine learning began through many fun experiences building and experimenting with bleeding-edge software early on, and competing in mathematics Olympiads in high school.

Research

My current research interests include machine learning, deep learning, computational biology, and generative modeling. In particular, I have developed new geometric graph neural network architectures such as GCPNet and the Geometric Transformer for modeling biomolecules of various sizes (e.g., proteins), introduced GCDM for diffusion generative modeling of 3D molecules, assembled PoseBench, the first deep learning benchmark for broadly applicable protein-ligand docking, and released FlowDock, the first all-atom flow matching model for protein-ligand structure and binding affinity prediction.

Writing

I enjoy sharing thoughts on literature, research, and applications on my blog.

Education

Looking to get started with machine learning?

Mentorship

Interested in chatting AI or active research problems?


news

Mar 13, 2025 One conditional acceptance by ISMB :dna:
Jan 22, 2025 One spotlight accepted by ICLR :sparkles:
Dec 03, 2024 Presented MULTICOM_ligand & FlowDock as top-ranked DL methods at CASP16 :dna:
Jul 03, 2024 One paper accepted by Nature CommsChem :dna:
Jun 17, 2024 Two ICML workshop papers selected for spotlight presentations :sparkles:

latest posts

selected publications

  1. Geometric Transformers for Protein Interface Contact Prediction
    Alex Morehead, Chen Chen, and Jianlin Cheng
    In The Tenth International Conference on Learning Representations (ICLR), 2022
    Presented a new line graph message passing transformer at ICLR 2022
  2. ISMB
    Gated_Graph_Transformer.jpeg
    A gated graph transformer for protein complex structure quality assessment and its performance in CASP15
    Xiao Chen*Alex Morehead*, Jian Liu, and 1 more author
    In Intelligent Systems for Molecular Biology (ISMB), 2023
    Follow-up work to Geometric Transformers, presented at ISMB 2023
  3. Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes
    Alex Morehead, Aadyot Bhatnagar, Jeffrey A. Ruffolo, and 1 more author
    In NeurIPS Machine Learning in Structural Biology (MLSB) Workshop, 2023
    First generative model of protein and nucleic acid biomolecules
  4. Geometry-Complete Perceptron Networks for 3D Molecular Graphs
    Alex Morehead, and Jianlin Cheng
    Bioinformatics, 2024
    Chirality-aware vector message passing, also presented at the AAAI 2023 DLG (poster) and AI2ASE (oral presentation) workshops
  5. Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
    Alex Morehead, and Jianlin Cheng
    Nature Communications Chemistry, 2024
    Chirality-aware diffusion generative model of 3D molecules, also presented at the ICLR 2023 MLDD workshop
  6. Evaluating Representation Learning on the Protein Structure Universe
    Arian R. Jamasb*Alex Morehead*, Chaitanya K. Joshi*, and 8 more authors
    In The Twelth International Conference on Learning Representations (ICLR), 2024
    Comprehensive benchmarking and experimentation suite for protein representation learning, also presented at the NeurIPS 2023 MLSB workshop
  7. Deep Learning for Protein-Ligand Docking: Are We There Yet?
    Alex Morehead, Nabin Giri, Jian Liu, and 1 more author
    In ICML AI4Science Workshop, 2024
    Comprehensive benchmarking and experimentation suite for protein-ligand docking and structure prediction, selected as a spotlight presentation (top 20% - 30/159)
  8. RNA-FrameFlow for de novo 3D RNA Backbone Design
    Rishabh Anand*, Chaitanya K Joshi*Alex Morehead, and 6 more authors
    In ICML AI4Science & SPIGM Workshops, 2024
    Conditional flow matching for geometric RNA structure design, selected as a SPIGM (AI4Science) oral (spotlight) presentation
  9. ISMB
    FlowDock.gif
    FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
    Alex Morehead, and Jianlin Cheng
    In Intelligent Systems for Molecular Biology (ISMB), 2025
    First all-atom flow matching model for protein-ligand docking, presented as a CASP16 top-5 method