Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 8123-8137· 0 citations· 34 references
Medicine
TL;DR
It is demonstrated that permeability emerges from ensemble reorganization rather than a single conformational transition, establishing a generalizable framework for cyclic peptide design and ADMET prediction.
Abstract
Cyclic peptides are promising therapeutic agents, but their clinical translation is often limited by poor membrane permeability arising from complex, solvent-dependent conformational ensembles. Here, we present a modeling framework inspired by molecular chameleon, integrating sequence and structural information by explicitly representing solvent-dependent ensembles in aqueous and nonpolar environments. The model achieves strong predictive performance (MAE = 0.29, R = 0.85, R2 = 0.70 on CycPeptMPDB) under random split and retains robust performance under scaffold split evaluation (MAE = 0.34, Pearson R = 0.68). Beyond molecule-level prediction, analysis of one-residue analog-pair in testing set shows that the model captures experimentally meaningful ΔPAMPA trends, supporting its utility for cyclic peptide lead optimization. Model interpretation highlights polarity shielding captured by ΔPSA3D as a key mechanistically interpretable feature associated with permeability. These results demonstrate that permeability emerges from ensemble reorganization rather than a single conformational transition, establishing a generalizable framework for cyclic peptide design and ADMET prediction.
This resource is designed to facilitate the development of deep learning models that incorporate 3D or 4D (trajectory- or ensemble-based) information to improve the prediction of cyclic peptide membrane permeability.
Wei Liu, Pham Hung Nguyen, Chandra S. Verma et al.· Scientific Data· 0 citations
ApexFold, an environment-conditioned AI framework that combines sequence representations with physicochemical descriptors of the surrounding medium to predict circular-dichroism-derived fractions of α-helical, β-like, and unstructured conformations is developed.
M. D. Torres, Hanqun Cao, César de la Fuente-Núñez· bioRxiv· 0 citations
MultiMol is a multimodal framework that integrates SMILES sequences, molecular images, molecular graphs, and 3D conformations through tailored pre-training tasks and a scalable fusion mechanism, and consistently outperforms existing methods in cyclic peptide permeability prediction.
Haowen Chen, Xiuxiu Chao, Weihao Ou et al.· IEEE journal of biomedical a...· 0 citations
RNA molecules explore heterogeneous conformational ensembles that are essential for their biological function and molecular recognition, yet this intrinsic flexibility poses a major challenge for structure-based drug discovery. In particular, the absence of well-defined binding pockets in static structures limits the identification of ligandable sites. Here, we present an integrative ensemble-based approach that combines enhanced-sampling molecular dynamics simulations with Nuclear Magnetic Resonance data to characterize the conformational landscape of the HIV-1 TAR RNA at atomic resolution. Starting from extensive sampling, we refined the resulting conformational distribution through maximum-entropy reweighting to achieve quantitative agreement with experimental data. Analysis of the reweighted ensemble reveals a diverse set of conformational substates, including compact arrangements that exhibit pocket features compatible with ligand recognition and overlap with known ligand-bound structures. At the same time, highly ligandable conformations, which are only marginally populated, might nonetheless be critical for RNA recognition. Our results demonstrate that integrative ensemble modeling can reveal pharmacologically relevant RNA conformations that are not apparent from experimental static structures, providing a framework for ensemble-based strategies in RNA-targeted drug discovery.
Stefano Bosio, Vincent Schnapka, Mattia Bernetti et al.· bioRxiv· 0 citations
Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states, provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.
Hassan Nadeem, D. Kleiman, Yu-Ming Zhou et al.· bioRxiv· 0 citations
It is demonstrated that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations, suggesting that AI-based ensemble generation could provide an accessible approach for exploring membrane protein dynamics and complement conventional molecular modelling approaches.
B. Clifton, Adam G Grieve, Robin A. Corey· bioRxiv· 0 citations
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