reinforcement learning

Navigating committor landscape of biomolecules with a general pairwise interaction model

A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.

The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows

Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake flows, this is often achieved through Reduced-Order Models (ROMs) that first compress high-dimensional velocity snapshots into a latent space and then learn a time- steppi...

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScor...

Actuator-aware reinforcement learning control of active suspension systems with implicit road-adaptive reward shaping

Reinforcement learning reshapes cooperation in interaction dependent asymmetric games

Path planning for underwater gliders combining acoustic field propagation characteristics and deep reinforcement learning

An Explicit Interaction-Prompted Diffusion Framework for High-Fidelity 3D Molecular Generation.

Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff...

Robust reachability within deep reinforcement learning framework

Tri-driven graph reinforcement learning for dynamic root cause tracing in UF-RO water treatment processes

LLM powered adaptive traffic signal control via knowledge-enhanced prompt and reinforcement learning fine-tuning

Reinforcement learning-enhanced barrier function super-twisting sliding mode control for bidirectional electric vehicle charging with superconducting magnetic energy storage

LOGO: A framework for language-based goal decomposition and skill recomposition in reinforcement learning