Search Results for author: Raphael J. L. Townshend

Found 7 papers, 3 papers with code

ATOM3D: Tasks On Molecules in Three Dimensions

3 code implementations7 Dec 2020 Raphael J. L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, Ron O. Dror

We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations.

Learning from Protein Structure with Geometric Vector Perceptrons

3 code implementations ICLR 2021 Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, Ron Dror

Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain.

Protein Design Relational Reasoning

Geometric Prediction: Moving Beyond Scalars

no code implementations25 Jun 2020 Raphael J. L. Townshend, Brent Townshend, Stephan Eismann, Ron O. Dror

This novel and data-efficient ability to predict real-world geometric tensors opens the door to addressing many problems through the lens of geometric prediction, in areas such as 3D vision, robotics, and molecular and structural biology.

Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes

no code implementations5 Jun 2020 Stephan Eismann, Raphael J. L. Townshend, Nathaniel Thomas, Milind Jagota, Bowen Jing, Ron O. Dror

Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery.

Drug Discovery

Transferrable End-to-End Learning for Protein Interface Prediction

no code implementations27 Sep 2018 Raphael J. L. Townshend, Rishi Bedi, Ron O. Dror

While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question.

Protein Interface Prediction Transfer Learning

End-to-End Learning on 3D Protein Structure for Interface Prediction

1 code implementation NeurIPS 2019 Raphael J. L. Townshend, Rishi Bedi, Patricia A. Suriana, Ron O. Dror

Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited.

Open-Ended Question Answering Protein Interface Prediction +1

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