- 🔭 I’m currently working on applications of geometric deep learning and generative modeling for materials science, molecular chemistry, as well as structural and cellular biology.
- 🌱 I’m currently learning about the intersection of generative modeling and representation learning for science and other data broadly speaking.
- 💬 Ask me about machine learning, artificial intelligence, computational biology/chemistry, music, cardio and strength training, etc.
- 📫 How to reach me: LinkedIn, Twitter, Gmail, etc.
- ⚡ Fun fact: I can understand (i.e., read) more Chinese and Japanese than I can speak.
-
Lawrence Berkeley National Laboratory
- Berkeley, California
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17:26
(UTC -08:00) - https://amorehead.github.io/
- https://orcid.org/0000-0002-0586-6191
- in/alexmorehead
- @MoreheadAlex
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BioinfoMachineLearning/GCPNet
BioinfoMachineLearning/GCPNet PublicA geometry-complete SE(3)-equivariant perceptron network (GCPNet) for 3D graphs. (Bioinformatics)
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BioinfoMachineLearning/bio-diffusion
BioinfoMachineLearning/bio-diffusion PublicA geometry-complete diffusion generative model (GCDM) for 3D molecule generation and optimization. (Nature CommsChem)
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BioinfoMachineLearning/DeepInteract
BioinfoMachineLearning/DeepInteract PublicA geometric deep learning framework (Geometric Transformers) for predicting protein interface contacts. (ICLR 2022)
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a-r-j/ProteinWorkshop
a-r-j/ProteinWorkshop PublicBenchmarking framework for protein representation learning. Includes a large number of pre-training and downstream task datasets, models and training/task utilities. (ICLR 2024)
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BioinfoMachineLearning/PoseBench
BioinfoMachineLearning/PoseBench PublicComprehensive benchmarking of protein-ligand structure prediction methods. (Nature Machine Intelligence)
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BioinfoMachineLearning/FlowDock
BioinfoMachineLearning/FlowDock PublicA geometric flow matching model for generative protein-ligand docking and affinity prediction. (ISMB 2025)
If the problem persists, check the GitHub status page or contact support.






