medical-4

Text-based de novo Molecule Generation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in text-based-de-novo-molecule-generation-4

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ChEBI-20

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ChEBI-20

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Most implemented papers

MolFM: A Multimodal Molecular Foundation Model

Zaiqing Nie, Yi Luo, Kai Yang, Massimo Hong, Xingyi Liu•Mon Jun 05 2023

This study introduces MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs, and provides theoretical analysis that MolFM captures local and global molecular knowledge by minimizing the distance in the feature space between different modalities of the same molecule.

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Translation between Molecules and Natural Language

Carl N. Edwards, Heng Ji, T. Lai, Kevin Ros, Garrett Honke•Sun Apr 24 2022

The results show that MolT5-based models are able to generate outputs, both molecules and captions, which in many cases are high quality, and consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation.

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Unifying Molecular and Textual Representations via Multi-task Language Modelling

O. Winther, Jannis Born, Dimitrios Christofidellis, Giorgio Giannone, T. Laino, Matteo Manica•Sat Jan 28 2023

This work proposes the first multi-domain, multi-task language model that can solve a wide range of tasks in both the chemical and natural language domains and suggests that such models can robustly and efficiently accelerate discovery in physical sciences by superseding problem-specific fine-tuning and enhancing human-model interactions.

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Empowering Molecule Discovery for Molecule-Caption Translation With Large Language Models: A ChatGPT Perspective

Jiliang Tang, Xiao Wei, Wenqi Fan, Hui Liu, Jiatong Li, Yunqing Liu, Qing Li•Sat Jun 10 2023

This work proposes a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning.

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GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text

Peng Liu, Yiming Ren, Zhixiang Ren•Sun Aug 13 2023

GIT-Mol is introduced, a multi-modal large language model that integrates the Graph, Image, and Text information and proposes GIT-Former, a novel architecture that is capable of aligning all modalities into a unified latent space.

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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations

Jinhua Zhu, Lijun Wu, Qizhi Pei, Kaiyuan Gao, Rui Yan, Yingce Xia, Wei Zhang, Kehan Wu•Tue Oct 10 2023

A comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations, and distinguishes between structured and unstructured knowledge, leading to more effective utilization of information.

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Text-Guided Molecule Generation with Diffusion Language Model

Q. Liu, Shu Wu, Liang Wang, Haisong Gong•Mon Feb 19 2024

It is demonstrated that TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for additional data resources, underscore the remarkable effectiveness of TGM-DLM in generating coherent and precise molecules with specific properties, opening new avenues in drug discovery and related scientific domains.

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BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning

Xiaozhuan Liang, Jinhua Zhu, Lijun Wu, Qizhi Pei, Kaiyuan Gao, Yin Fang, Shufang Xie, Tao Qin, Rui Yan•Mon Feb 26 2024

BioT5+ is introduced, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery, and stands out for its ability to capture intricate relationships in biological data, thereby contributing significantly to bioinformatics and computational biology.

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