From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning

• URL: http://arxiv.org/abs/2311.02798v2
• Date: Mon, 1 Jul 2024 02:19:36 GMT
• Title: From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning
• Authors: Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia,
• Abstract summary: This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge. Our approach demonstrates competitive performance across various molecular property benchmarks.
• Score: 10.025809630976065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.

Related papers

• Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge [14.08112359246334]
  We present MV-Mol, a representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs. We show that MV-Mol provides improved representations that substantially benefit molecular property prediction.
  arXiv  Detail & Related papers  (2024-06-14T08:48:10Z)
• Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions [0.0]
  We introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously.
  arXiv  Detail & Related papers  (2024-04-05T17:15:48Z)
• Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model [49.64512917330373]
  We introduce a multi-constraint molecular generation large language model, TSMMG, akin to a student. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers' We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements.
  arXiv  Detail & Related papers  (2024-03-20T02:15:55Z)
• Interactive Molecular Discovery with Natural Language [69.89287960545903]
  We propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules. To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information.
  arXiv  Detail & Related papers  (2023-06-21T02:05:48Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• Domain-Agnostic Molecular Generation with Chemical Feedback [44.063584808910896]
  MolGen is a pre-trained molecular language model tailored specifically for molecule generation. It internalizes structural and grammatical insights through the reconstruction of over 100 million molecular SELFIES. Our chemical feedback paradigm steers the model away from molecular hallucinations, ensuring alignment between the model's estimated probabilities and real-world chemical preferences.
  arXiv  Detail & Related papers  (2023-01-26T17:52:56Z)
• Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space [0.0]
  We introduce a novel approach for the active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data. This approach combines the elements from the symbolic regression methods such as SISSO and active learning into a single framework.
  arXiv  Detail & Related papers  (2023-01-06T14:22:43Z)
• A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language [63.60376252491507]
  We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data. We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
  arXiv  Detail & Related papers  (2022-09-12T00:56:57Z)
• Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [68.8204255655161]
  We introduce a novel framework for scalable 3D design that uses a hierarchical agent to build molecules. In a variety of experiments, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms.
  arXiv  Detail & Related papers  (2022-02-01T18:54:24Z)
• Knowledge-aware Contrastive Molecular Graph Learning [5.08771973600915]
  We propose Contrastive Knowledge-aware GNN (CKGNN) for self-supervised molecular representation learning. We explicitly encode domain knowledge via knowledge-aware molecular encoder under the contrastive learning framework. Experiments on 8 public datasets demonstrate the effectiveness of our model with a 6% absolute improvement on average.
  arXiv  Detail & Related papers  (2021-03-24T08:55:08Z)

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.