6 accepted papers
Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Despite fruitful progress, existing methods still face key limitations: classical computational approaches remain prohibitively expensive due to their …
Molecular Dynamics (MD) simulations provide a fundamental tool for characterizing molecular behavior at full atomic resolution, but their applicability is severely constrained by the computational cost. To address this, a surge of deep generative models has recently emerged to learn dynamics at coar…
To address the issue that current transformable spoke-wheeled leg-wheel robots cannot simultaneously achieve simple structure, stable locomotion and open step climbing ability, a novel robot design method is proposed. The robot called BiTSpoke is driven by two transformable spoke wheels, with a pass
Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their transitions and interactions. However, classical MD techniques are limited by the trade-off between accuracy and efficiency, while recent deep learning-based…
LiDAR point cloud data is widely utilized in autonomous driving systems and has significantly improved the 3D detection performance with well-designed deep neural network models. However, due to the complexity of real-world environments and model vulnerability, false detections or malicious attacks…
The study of rigid protein-protein docking plays an essential role in a variety of tasks such as drug design and protein engineering. Recently, several learning-based methods have been proposed for the task, exhibiting much faster docking speed than those computational methods. In this paper, we pro…