MCTH (Monte Carlo Tree Hallucination), an inference-only framework that casts all-atom sequence-structure co-design as uncertainty-aware planning over hallucinated states from pretrained folding and inverse-folding models, with optional biophysical control within the same decision loop is introduced.
Abstract
Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/RNA, underexplored non-protein modalities with scarce, heterogeneous complex data and sharper geometric and chemical constraints. We introduce MCTH (Monte Carlo Tree Hallucination), an inference-only framework that casts all-atom sequence-structure co-design as uncertainty-aware planning over hallucinated states from pretrained folding and inverse-folding models, with optional biophysical control within the same decision loop. MCTH treats these models as frozen black-box operators and uses Monte Carlo Tree Search to allocate a fixed inference budget across competing design trajectories, incorporating model confidence and uncertainty, as well as cross-expert consensus/disagreement when multiple predictors are available. Across protein-RNA, protein-DNA, protein-protein, and protein-ligand design, matched-budget experiments show that adaptive search improves over simpler sampling and cycling strategies, while held-out AlphaFold3 and Chai-1 evaluations demonstrate transfer beyond the search-time oracle. MCTH provides a shared planning layer across modalities while allowing task-specific folding, inverse-folding, and biophysical modules, requiring no fine-tuning or backpropagation through component models.
The results establish Molexar as a practical unified foundation for computational chemistry and drug-design workflows and establish Molexar as a practical unified foundation for computational chemistry and drug-design workflows.
Haoyu Lin, Yiyan Liao, Jinmei Pan et al.· 0 citations
By merging chemical heuristics with physical laws, DrugRPG effectively addresses the hallucination crisis, shifting generative SBDD from scoring-oriented optimization toward high-fidelity, realistic lead discovery.
Elucidating protein dynamics is crucial for deciphering fundamental biological processes, from enzyme catalysis to cellular signaling, as its dysregulation directly causes protein misfolding diseases such as Alzheimer's and Parkinson's. While artificial intelligence has revolutionized static protein structure prediction, capturing the high-dimensional dynamics of protein folding remains a formidable challenge that limits our ability to fully understand these vital biological phenomena. Here we present DA2-GRASP, a computational framework that overcomes this barrier by integrating deep learning with advanced sampling techniques to map protein folding pathways with unprecedented efficiency and accuracy. DA2-GRASP learns low-dimensional latent representations of protein conformations via a variational autoencoder and combines multidirectional generative sampling guided by local potential energy gradients to efficiently steer conformational transitions along energetically favorable paths, enabling accurate and efficient reconstruction of folding pathways. Our method achieves sublinear computational scaling with sequence length, contrasting the quadratical scaling of molecular dynamics-based conventional approaches, enabling tractable simulations. It maintains high precision in quantifying mutation-induced perturbations to folding thermodynamics, crucial for understanding disease mutations. It also enables atomistic characterization of the folding process of medium-sized proteins such as ubiquitin and small ubiquitin-like modifier (SUMO, ∼80 residues) on standard workstations, a task typically requiring specialized supercomputing platforms such as Anton. Analysis of these proteins provides new mechanistic insights into how structurally similar folds with low sequence identity navigate divergent folding pathways. DA2-GRASP thus establishes a versatile and powerful framework for exploring protein-folding dynamics and their functional consequences.
Yanbing Wen, Hao Dong· Journal of Chemical Theory a...· 0 citations
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
Guodong Min, Huan Peng· Methods in molecular biology· 0 citations
Deep generative models have become a leading approach for designing therapeutic molecules, yet efficiently exploring vast biomolecular sequence spaces remains difficult, particularly for targets with limited training data. The prior distribution that seeds a generative model shapes which regions of sequence space it explores, and recent work suggests that non-classical distributions sampled from quantum processors can serve as a structured alternative to the factorised Gaussian priors used by default. Whether such priors help on complex biological design tasks has been largely untested. Here we present what is, to our knowledge, the first end-to-end hybrid quantum-classical pipeline for de novo design of MHC class I-binding peptides, coupling a generative adversarial network (GAN) to latent vectors sampled from a real photonic quantum processor. Tested in silico across 131 HLA alleles, quantum-derived priors increased the yield of predicted strong binders, with the largest relative gains for understudied alleles where classical baselines perform worst. We selected three understudied alleles for further evaluation, finding that large gains coincided with broader sequence exploration at non-anchor positions while anchor specificity was preserved. On these three alleles, we validated the designs in vitro using peptide-MHC stability ELISAs, confirming that quantum-designed peptides are potent stabilisers of peptide-MHC class I complexes. These results establish structured, hardware-realisable non-classical priors as a useful inductive bias for generative peptide design, with direct relevance to personalised immunotherapies and vaccines.
E. S. Engdal, Jonathan Funk, O. Bacarreza et al.· bioRxiv· 0 citations
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.