Aug 2026· Bioinformatics· Vol 42· 0 citations· 24 references
Medicine
TL;DR
MARD-Mol is proposed, a hybrid AR-diffusion framework based on motif-inspired units that reformulate property optimization into an iterative “diagnose-and-repair” process, enabling targeted optimization of defective motifs while preserving the global scaffold.
Abstract
Abstract Motivation Deep generative models have transformed drug molecule generation. However, molecules exhibit complex hierarchical structures, requiring models to simultaneously balance macroscopic topological coherence and microscopic chemical self-consistency. Although autoregressive (AR) and discrete diffusion paradigms are highly complementary, integrating their advantages within a unified architecture remains severely limited by traditional “atom-by-atom” fine-grained modeling. Results We propose MARD-Mol, a hybrid AR-diffusion framework based on motif-inspired units. By elevating the representation granularity from atoms to motif-inspired units and introducing a dual-stream hierarchical attention mechanism, it couples inter-unit AR global scaffold planning with intra-unit discrete diffusion generation. To support goal-directed drug discovery, we reformulate property optimization into an iterative “diagnose-and-repair” process, enabling targeted optimization of defective motifs while preserving the global scaffold. Extensive experiments demonstrate that MARD-Mol achieves an 86.0% Quality score in de novo generation and exhibits superior performance in fragment-constrained and multi-objective optimization, establishing a new paradigm for high-quality drug design. Availability and implementation The source code and datasets used in this study are available at GitHub: https://github.com/CSUBioGroup/MARD-Mol.
Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require the molecular size to be specified a priori. Recent autoregressive approaches have substantially narrowed the performance gap while naturally supporting variable-length generation and conditioning on partial molecular context. However, balancing unconditional and context-conditioned generation remains challenging. We introduce KRONOS, a latent autoregressive diffusion framework that generates molecules in the latent space of a pre-trained autoencoder, jointly modeling molecular graph topology and geometry, while retaining the flexibility of autoregressive generation. We further introduce a mixed training strategy inspired by Fill-in-the Middle (FIM) paradigm, enabling both unconditional and fragment-conditioned molecular generation within a single left-to-right autoregressive model. Experiments on QM9 and GEOM-Drugs demonstrate that KRONOS achieves leading unconditional generation performance among autoregressive methods, while remaining competitive with diffusion models. Moreover, fragment-conditioned generation is achieved with negligible impact on unconditional generation performance, demonstrating that both generation paradigms can be supported within a single architecture.
Federico Ottomano, Gaopeng Ren, Yingzhen Li et al.· 0 citations
The design of molecules with desired properties is a key challenge in drug discovery and materials science. Traditional methods rely on trial-and-error, while recent deep-learning approaches accelerate molecular generation. However, existing models struggle with generating molecules based on specific textual descriptions. We introduce Mol-CADiff, a diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation. Our approach explicitly models the causal relationship between textual prompts and molecular structures, overcoming limitations in existing methods. We enhance dependency modeling both within and across modalities, enabling precise control over the generation process. While primarily designed for text-guided tasks, this architecture inherently supports unconditional generation, providing the added capability to autonomously sample the broader chemical space without explicit constraints. Here we show that Mol-CADiff outperforms alternative methods in generating diverse, chemically valid molecules, with better alignment to specified properties, enabling more intuitive language-driven molecular design. By bridging these modalities, our framework provides a versatile method for drug discovery. Computational approaches to molecular design often explore only limited regions of the vast chemical space. This study presents a causality-aware diffusion model that generates valid and diverse molecules with or without text prompts, improving controllability in molecular design.
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GeoNet is a physicochemical-principle-guided framework for modeling dual-range atomic interactions that achieves the smallest model size and the shortest training time, demonstrating both superior predictive performance and computational efficiency.
Generative models are emerging as a key technology for accelerating molecular discovery in drug design, materials science, and catalysis by enabling efficient exploration of the vast chemical space of possible molecules. Recent advances in deep generative modeling—including variational autoencoders (VAEs), diffusion models, flow matching methods, and autoregressive transformer-based approaches—have produced a diverse toolkit for generating molecular structures and optimizing their properties. However, these paradigms are often studied independently, leaving many machine learning researchers without a clear understanding of their connections, strengths, and limitations in molecular applications. This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures. While the tutorial primarily focuses on generative models for de novo molecular design, we also briefly discuss how similar modeling paradigms extend to reaction prediction and retrosynthesis. By presenting these models within a common framework, the tutorial aims to equip ML researchers and AI-for-science practitioners with a clear conceptual map of the generative modeling landscape for molecular discovery and identify emerging research opportunities in this rapidly evolving area.
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This review focuses on coordinate- and residue-frame-based diffusion approaches for generating protein structures, paying particular attention to geometric equivariance, conditioning strategies, all-atom modelling and interaction-aware design.
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