Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning

• URL: http://arxiv.org/abs/2410.04223v1
• Date: Sat, 5 Oct 2024 16:35:32 GMT
• Title: Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
• Authors: Gang Liu, Michael Sun, Wojciech Matusik, Meng Jiang, Jie Chen,
• Abstract summary: Large language models (LLMs) have integrated images, but adapting them to graphs remains challenging. We introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning.
• Score: 32.745100532916204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty stems from the need for coherent autoregressive generation across texts and graphs. To address this, we introduce Llamole, the first multimodal LLM capable of interleaved text and graph generation, enabling molecular inverse design with retrosynthetic planning. Llamole integrates a base LLM with the Graph Diffusion Transformer and Graph Neural Networks for multi-conditional molecular generation and reaction inference within texts, while the LLM, with enhanced molecular understanding, flexibly controls activation among the different graph modules. Additionally, Llamole integrates A* search with LLM-based cost functions for efficient retrosynthetic planning. We create benchmarking datasets and conduct extensive experiments to evaluate Llamole against in-context learning and supervised fine-tuning. Llamole significantly outperforms 14 adapted LLMs across 12 metrics for controllable molecular design and retrosynthetic planning.

Related papers

• G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models [15.32011692129901]
  We introduce G2T-LLM, a novel approach that uses graph-to-tree text encoding to transform graph-based molecular structures into a hierarchical text format optimized for benchmark (LLMs) This encoding converts complex molecular graphs into tree-structured formats, such as large language models and XML, which LLMs are particularly adept at processing due to their extensive pre-training on these types of data. Through supervised fine-tuning, G2T-LLM generates valid and coherent chemical structures, addressing common challenges like invalid outputs seen in traditional graph-based methods.
  arXiv  Detail & Related papers  (2024-10-03T04:25:21Z)
• Many-Shot In-Context Learning for Molecular Inverse Design [56.65345962071059]
  Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) We develop a new semi-supervised learning method that overcomes the lack of experimental data available for many-shot ICL. As we show, the new method greatly improves upon existing ICL methods for molecular design while being accessible and easy to use for scientists.
  arXiv  Detail & Related papers  (2024-07-26T21:10:50Z)
• MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension [34.586861881519134]
  Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields. This study seeks to enhance the ability of LLMs to comprehend molecules by equipping them with a multi-modal external module, namely MolX. In particular, instead of directly using a SMILES string to represent a molecule, we utilize specific encoders to extract fine-grained features from both SMILES string and 2D molecular graph representations.
  arXiv  Detail & Related papers  (2024-06-10T20:25:18Z)
• LDMol: Text-to-Molecule Diffusion Model with Structurally Informative Latent Space [55.5427001668863]
  We present a novel latent diffusion model dubbed LDMol for text-conditioned molecule generation. LDMol comprises a molecule autoencoder that produces a learnable and structurally informative feature space. We show that LDMol can be applied to downstream tasks such as molecule-to-text retrieval and text-guided molecule editing.
  arXiv  Detail & Related papers  (2024-05-28T04:59:13Z)
• Data-Efficient Molecular Generation with Hierarchical Textual Inversion [48.816943690420224]
  We introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method. HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution. Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
  arXiv  Detail & Related papers  (2024-05-05T08:35:23Z)
• Benchmarking Large Language Models for Molecule Prediction Tasks [7.067145619709089]
  Large Language Models (LLMs) stand at the forefront of a number of Natural Language Processing (NLP) tasks. This paper explores a fundamental question: Can LLMs effectively handle molecule prediction tasks? We identify several classification and regression prediction tasks across six standard molecule datasets. We compare their performance with existing Machine Learning (ML) models, which include text-based models and those specifically designed for analysing the geometric structure of molecules.
  arXiv  Detail & Related papers  (2024-03-08T05:59:56Z)
• Large Language Models are In-Context Molecule Learners [22.06735237464927]
  We propose In-Context Molecule Adaptation (ICMA), as a new paradigm allowing LLMs to learn the molecule-text alignment from context examples. ICMA incorporates the following three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning. We show that ICMT can empower LLMs to achieve state-of-the-art or comparable performance without extra training corpora and intricate structures.
  arXiv  Detail & Related papers  (2024-03-07T03:58:28Z)
• MolTC: Towards Molecular Relational Modeling In Language Models [28.960416816491392]
  We propose a novel framework for Molecular inTeraction prediction following Chain-of-Thought (CoT) theory termed MolTC. Our experiments, conducted across various datasets involving over 4,000,000 molecular pairs, exhibit the superiority of our method over current GNN and LLM-based baselines.
  arXiv  Detail & Related papers  (2024-02-06T07:51:56Z)
• Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [57.052160123387104]
  We present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-10-27T14:00:04Z)
• MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter [91.77292826067465]
  Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception. We propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter.
  arXiv  Detail & Related papers  (2023-10-19T14:52:58Z)
• Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective [53.300288393173204]
  Large Language Models (LLMs) have shown remarkable performance in various cross-modal tasks. In this work, we propose an In-context Few-Shot Molecule Learning paradigm for molecule-caption translation. We evaluate the effectiveness of MolReGPT on molecule-caption translation, including molecule understanding and text-based molecule generation.
  arXiv  Detail & Related papers  (2023-06-11T08:16:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.