EDWARD: E(3)-Equivariant Dual-Way Attentive Reduction for Peptide-to-Small-Molecule Design

Peptides, valued for their high affinity and selectivity
yet hindered
by suboptimal pharmacokinetics, are important entry points for lead
discovery; the canonical strategy preserves their key binding interactions
and prunes them into developable small molecules. However, pharmacophore-based
simplification methods heavily depend on high-quality databases and
expert knowledge, and pocket-conditioned generation alone struggles
to align critical pharmacophores and orientations, limiting the controllability
and efficiency. We therefore introduce EDWARD, a geometry–pharmacophore
joint framework that learns “pharmacophore cloud clusters”
and radial-directional constraints from peptide–protein complexes,
and performs geometry-first assembly of modular fragments and multianchor
scaffolds, thereby avoiding end-to-end scaffold learning. This strategy
stably maintains alignment and pocket compatibility across conformations,
lengths, and topologies; achieves a better balance among affinity
retention, drug-likeness, and diversity; and requires little annotation
with controllable computational cost, providing a supplementary route
from peptide cues to translatable small molecules.