6 accepted papers
Rich data and powerful machine learning models allow us to design drugs for a specific protein target <em>in silico</em>. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free models as the atomic interaction in the 3D space is explicitl…
Enlightened by the fast-running gait of mammals like cheetahs and wolves, we design and fabricate a single-actuated untethered compliant robot that is capable of galloping at a speed of 313 mm/s or 1.56 body length per second (BL/s), faster than most reported soft crawlers in mm/s and BL/s. An in-pl…
Robot grasping is subject to an inherent tradeoff: Grippers with a large span typically take a longer time to close, and fast grippers usually cover a small span. However, many practical applications of grippers require the ability to close a large distance rapidly. For example, grasping cloth typic…
Protein-protein interactions are crucial to many biological processes, and predicting the effect of amino acid mutations on binding is important for protein engineering. While data-driven approaches using deep learning have shown promise, the scarcity of annotated experimental data remains a major c…
Antibodies are immune system proteins that protect the host by binding to specific antigens such as viruses and bacteria. The binding between antibodies and antigens is mainly determined by the complementarity-determining regions (CDR) of the antibodies. In this work, we develop a deep generative mo…
Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and expressiveness --- some are designed ad hoc for specific data types, some are too complex to be accessible, and some sacr…