Title: OpenPhase: Condition-Aware Exploration of Multicomponent Biosystem Phase-Separating Behavior
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: AI for Biology, Deep learning, Protein Liquid-liquid phase separation, Protein design

Abstract: Liquid-liquid phase separation (LLPS) is a fundamental biophysical process
      in which biomolecules, such as proteins, DNA and RNA, demix from solution
      to form distinct liquid phases. Crucially, experimental conditions, such as
      salt, temperature, pH and concentration, profoundly influence LLPS
      properties and dynamics, often determining whether phase separation occurs
      and modulating the propensity of the resulting biomolecular condensates.
      While numerous machine learning methods have been developed for protein
      phase behavior prediction, their capabilities are frequently constrained by
      the inherent complexity of biosystems and the vast variability of environmental
      conditions. Here, we introduce \textbf{OpenPhase}, the first condition-aware
      platform for system-level exploration of biomolecular phase behavior.
      OpenPhase uniquely provides well-structured and ready-to-use datasets of
      experimentally verified phase outcomes coupled with corresponding
      conditions. We also formalize three canonical tasks (1) condition-aware phase
      outcome prediction, (2) condition inference of LLPS, and (3) conditional phase-separating
      system design. These tasks well articulate the interplay between system
      components, environmental conditions, and emergent phase properties. For each
      task, we propose novel solutions and benchmark them against strong
      baseline models. OpenPhase also includes a user-friendly API to facilitate
      \textit{in-silico} development and evaluation of novel machine learning
      methods for complex biosystem phase behavior modeling.

TL;DR: Condition-Aware prediction for Biomolecular Phase Behavior