Skip to content
Log in
Book Open access

PRIME: A Pretrained Representation-Induced Model for 3D Molecules in De Novo Binder Design

Zhihua Tian Jiale Zhou Rubo Wang Yidong Song Zhenchao Tang Tianxu Lv Zhijian Wu Yefeng Zheng
Aug 2026 · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 · 0 citations · 15 references

TL;DR

Semantics-Preserving Exploratory Sampling (SPES), which integrates Graph Laplacian Spectral Noise to respect chain connectivity and Conditional Freedom Modulation to dynamically balance exploration with fidelity, enables diversity-enhanced generation without sacrificing geometric validity under the reported structural metrics, improving the empirical exploration--fidelity trade-off.

Abstract

Biomolecular binder design for peptides and antibodies requires generating diverse candidates that satisfy stringent three-dimensional geometric constraints while enabling affinity-oriented exploration under strong structural priors. In current generative models, the effective search space for structurally feasible binders is severely constrained, as the complexity of biochemical interactions is not explicitly encoded into a semantically grounded representation of viable molecular manifolds. To address this challenge, we propose Pretrained Representation Induced Molecular gEneration (PRIME), a unified generative framework for three-dimensional binder design across peptides and antibodies. PRIME grounds stochastic generation on frozen large-scale pretrained structural representations, inheriting robust physical priors to ensure structural feasibility without training a manifold from scratch. However, defining a feasible space alone is insufficient for effective exploration. Under commonly used isotropic perturbations, chain topology is ignored, allowing local noise to propagate into global structural distortions. To enable controlled exploration within the feasible space, we introduce Semantics-Preserving Exploratory Sampling (SPES), which integrates Graph Laplacian Spectral Noise to respect chain connectivity and Conditional Freedom Modulation to dynamically balance exploration with fidelity. By aligning stochastic exploration with structural semantics, PRIME enables diversity-enhanced generation without sacrificing geometric validity under the reported structural metrics, improving the empirical exploration--fidelity trade-off. PRIME achieves state-of-the-art performance on unified peptide and antibody benchmarks, effectively reconciling geometric validity with functional optimization under computational proxy metrics. The source code is available at https://github.com/simplaj/PRIME.

DOI Semantic Scholar
Read PDF

Similar papers

Open access Aug 2026

Hierarchical discrete representations for coarse-to-fine protein conformation generation

This work proposes a novel approach that learns hierarchical discrete representations of protein structures using vector quantization, and outperforms state-of-the-art models such as ESMDiff across challenging benchmark datasets, including BPTI MD trajectories and conformational-changing pairs.

Seokjun On, Yujin Jeong, Kanghyeon Kim et al. · 0 citations
Book Open access Aug 2026

TGD-CSP: Reliable Crystal Structure Prediction with Template-Guided Diffusion and Energy-Based Optimization

Crystal Structure Prediction (CSP), the task of determining stable atomic arrangements from chemical composition alone, remains a central challenge in computational materials science with direct implications for accelerating materials discovery. While recent diffusion-based generative models achieve impressive results by conditioning on space-group symmetry information, this paradigm exposes three fundamental challenges: (1) unreliable symmetry inference from composition, (2) reliance on symmetry-only priors without comprehensive structural geometric guidance, and (3) prior-induced distribution shift caused by inaccurate or overly strong constraints. To address these challenges, we propose TGD-CSP, a three-stage generative framework that: (1) learns a cross-modal embedding space to retrieve structurally relevant templates directly from composition, thereby providing reliable symmetry priors; (2) guides diffusion-based generation via score-based conditioning that explicitly incorporates comprehensive geometric information from retrieved templates; and (3) fine-tunes the generative policy via reinforcement learning with an energy-based reward to alleviate prior-induced distribution shift and mitigate biased generation. % Evidence: Concrete results TGD-CSP achieves match rates of 74.56% and 75.25% on Perov-5 and MP-20, respectively, including a 42% relative improvement over state-of-the-art methods on Perov-5. It reduces RMSE to 0.0259 and 0.0258, respectively, while yielding structures with formation energies closely matching those of ground-truth structures. Our results demonstrate that TGD-CSP enables reliable generation from composition alone, significantly improving the practicality of generative models for real materials discovery.

Lu Yang, Tiantian Xu, X. Liu et al. · 0 citations
Preprint Aug 2026

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

Packora is presented, a flow-based generative model for molecular CSP that jointly predicts atomic coordinates and the lattice from molecular graphs that outperforms the baselines on both structure generation and ranking benchmarks.

Nayoung Kim, Kiyoung Seong, Sungsoo Ahn · 0 citations
Aug 2026

PINS: A Physics-Informed Generative Framework for De Novo Structure Elucidation from 1D NMR Spectra.

PINS (Physics-Informed NMR Structure elucidation model), a generative framework that explicitly bridges the gap between spectral data and molecular topology by enforcing multiphysical priors, provides a trustworthy, automated strategy for decoding novel chemical structures in data-scarce regimes.

Pengfei Liu, Cuimei Liu, Laiqun Xia et al. · 0 citations
Open access Jul 2026

HELM-BERT: Topology-Aware Representations for Chemically Modified Peptides

These findings suggest that pretraining directly on notation that makes structural constraints explicit offers a transferable strategy for biomolecular modalities that fall between small-molecule chemistry and protein sequence.

Seungeon Lee, Takuto Koyama, Itsuki Maeda et al. · 0 citations