Mol-CADiff is introduced, a diffusion-based framework that uses causal attention mechanisms for text-conditional molecular generation and enhances dependency modeling both within and across modalities, enabling precise control over the generation process.
Abstract
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.
Text-guided molecule generation enables controlled molecular design from natural language descriptions and has broad applications in areas such as drug discovery. While recent methods have demonstrated promising capability in generating molecules that align well with textual descriptions, they often overlook the structural properties of the generated graphs. As a result, these approaches struggle to simultaneously ensure consistency with the input text and high structural quality of the generated molecules. In this paper, we propose a text-guided molecular graph generation framework that leverages the structural modeling power of graph diffusion models to achieve both strong alignment with textual descriptions and high-quality molecular structures. However, accomplishing this goal involves several key challenges: 1) how to align graph diffusion models with natural language instructions in order to generate molecular graphs with expected relational semantics from text, 2) how to directly optimize the quality of the generated molecular graphs without sacrificing fine-grained alignment with text-specific details. To tackle these challenges, we introduce Text-guided Conditional Discrete Graph Diffusion (TDGD), a discrete diffusion-based framework for generating molecular graphs from natural language descriptions. Our model incorporates a structure-aware cross-attention mechanism that aligns textual semantics with molecular structures by capturing relational semantics between textual descriptions and molecular structures. In addition, we propose a molecule structure consistency loss that explicitly enforces structural coherence during generation, leading to higher-quality and more consistent molecular graphs. Extensive experiments on ChEBI-20 and L+M-24 datasets demonstrate the effectiveness of our proposed TDGD model.
Yang Yao, Xin Wang, Yaofei Wu et al.· Proceedings of the 32nd ACM...· 0 citations
MolecularCanvas is an interactive system that enables users to iteratively construct an optimization context by integrating high-level goals, structure-level annotations, property constraints, and reference-based preferences that guides the generation of candidate molecules across diverse molecular structures.
Haoyu Dong, Rui Sheng, Shu-Hao Zhang et al.· 0 citations
Monroe is presented, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry dataset; improved graph representation of stereochemistry; improved training losses including conformer denoising and embedding decorrelation; improved multi-task learning; and the use of a prior-data-fitted model (TabPFN) for downstream in-context prediction.
Blazej Banaszewski, Andrew W. Fitzgibbon· 0 citations
This is the first method to expose GNN-derived attributions to an LLM as evidence for property prediction, and achieves the best overall results among generalist models and narrows the gap to specialist models tuned for each task.
Junwoo Park, Minyoung Shin, C. Lee et al.· 0 citations
Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.
Huabin Du, Mingyang Wang, M. Luo et al.· Journal of the American Chem...· 0 citations
A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.
Thomas MacDougall, Maksim Kuznetsov, Roman Schutski et al.· arXiv.org· 1 citation
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