![The study demonstrates that accurate prediction of protein-ligand binding affinity relies on precise consideration of functional group interactions-specifically, the affinity between carbonyl and pyridine groups-as neglecting these constraints leads to erroneous predictions of weak interactions, such as those incorrectly posited between carbon and oxygen atoms [latex] (C \leftrightarrow O) [/latex] instead of the correct interaction between carbon and nitrogen [latex] (C \leftrightarrow N) [/latex].](https://arxiv.org/html/2602.05479v1/x1.png)
Researchers have developed a novel deep learning framework to more accurately predict how small molecules interact with proteins, a crucial step in drug discovery and understanding biological processes.
A new communication framework leverages the power of large language models to enable more effective teamwork between robotic agents.
Researchers have developed a new framework enabling a single AI agent to control diverse humanoid robots, significantly simplifying the challenge of robotic locomotion.
A new review examines how large language models are being used to automate and improve the creation of software documentation and modeling.
Researchers have developed a new method for understanding the complex decision-making processes within autonomous AI systems.
New research reveals how people interpret and react to errors made by robots operating in public spaces, highlighting the complexities of real-world human-robot interaction.
New research sheds light on the inner workings of the ESMFold model, revealing the computational steps behind its remarkable ability to predict protein structures.
New research reveals a significant gap between AI performance on standard benchmarks and its ability to handle complex, real-world tasks in 3D environments.
A new approach combines path sampling with artificial intelligence to automatically identify key molecular pathways and dramatically speed up the study of infrequent, yet crucial, events.
![The autonomous system demonstrates graceful navigation within a dense pedestrian environment by aligning with prevailing flow-initially tracking forward movement and subsequently adapting to avoid opposing groups-achieving continued, safe progress as evidenced by its trajectory between [latex]t=4[/latex] and [latex]t=17[/latex].](https://arxiv.org/html/2602.05608v1/figure/robot_poster_image.png)
A new framework allows robots to navigate crowded environments by predicting pedestrian movement and aligning with natural flow.