13 accepted papers
Retrosynthesis prediction is a cornerstone of drug discovery, enabling the synthesis of novel therapeutic candidates. However, current deep learning models falter when navigating the unexplored chemical space essential for innovation. In these realistic scenarios, models face a challenging mixed out
Prevailing quantization techniques in Learned Image Compression (LIC) typically employ a static, uniform bit-width across all layers, failing to adapt to the highly diverse data distributions and sensitivity characteristics inherent in LIC models. This leads to a suboptimal trade-off between perform
Precise property prediction of organic materials is pivotal for next-generation electronic and energy devices. In density functional theory (DFT), the electron density (ED) serves as the fundamental determinant of material properties. Yet, establishing it as an input modality for material property p…
Whole-Slide Images (WSIs) are widely used for estimating the prognosis of cancer patients. Current studies generally follow a cancer-specific learning paradigm. However, the available training samples for one cancer type are usually scarce in pathology. Consequently, the model often struggles to lea
Identifying suitable reaction conditions is critical for chemical synthesis, as they directly affect yield, selectivity, and transformation feasibility. While recent methods have shown promising results, most approaches either encode reactants and products independently or rely on rule-based reactio
Multi-step retrosynthetic planning aims to decompose target molecules into available starting materials by iteratively invoking single-step prediction models within a search algorithm. The success of retrosynthetic planning depends on the joint guidance of single-step reasoning and global search acr
Driven by the "scale-is-everything" paradigm, modern machine learning increasingly demands ever-larger datasets and models, yielding prohibitive computational and storage requirements. Dataset distillation mitigates this by compressing an original dataset into a small set of synthetic samples, while…
Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense bio
Molecular design inherently involves the optimization of multiple conflicting objectives, such as enhancing bio-activity and ensuring synthesizability. Evaluating these objectives often requires resource-intensive computations or physical experiments. Current molecular design methodologies typically…
Identifying drug-drug interactions (DDIs) is critical for ensuring drug safety and advancing drug development, a topic that has garnered significant research interest. While existing methods have made considerable progress, approaches relying solely on known DDIs face a key challenge when applied to…
While a number of controllers exist for dynamic humanoid walking over known uneven terrain, the ability to negotiate moderate changes in ground height without environment perception is still lacking. Such capability would mitigate problems caused by inaccurate sensing and reduce online terrain-depen
This paper presents a method for the generation of dynamic walking gaits with a 3D Dual-SLIP model and its application to a simulated Hubo+ based humanoid. Previous approaches with the Dual-SLIP model have only focused on the planar case, wherein self-stable gaits can be found. When extended to 3D h…
The Dual-SLIP model has been proposed as a walking template that inherently encodes a rich set of human-like features. Previous work has used the 3D Dual-SLIP with bio-inspired leg actuation to generate a human-like dynamic walking gait over a wide range of speeds. The work presented in this paper e…