CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening

• URL: http://arxiv.org/abs/2502.11001v1
• Date: Sun, 16 Feb 2025 05:45:19 GMT
• Title: CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening
• Authors: Gen Zhou, Sugitha Janarthanan, Yutong Lu, Pingzhao Hu,
• Abstract summary: We introduce CL-MFAP, an unsupervised contrastive learning (CL)-based multimodal foundation (MF) model specifically tailored for discovering small molecules with potential antibiotic properties (AP) This model employs 1.6 million bioactive molecules with drug-like properties from the ChEMBL dataset to jointly pretrain three encoders. The CL-MFAP outperforms baseline models in antibiotic property prediction by effectively utilizing different molecular modalities and demonstrates superior domain-specific performance when fine-tuned for antibiotic-related property prediction tasks.
• Score: 9.162517838181683
• License:
• Abstract: Due to the rise in antimicrobial resistance, identifying novel compounds with antibiotic potential is crucial for combatting this global health issue. However, traditional drug development methods are costly and inefficient. Recognizing the pressing need for more effective solutions, researchers have turned to machine learning techniques to streamline the prediction and development of novel antibiotic compounds. While foundation models have shown promise in antibiotic discovery, current mainstream efforts still fall short of fully leveraging the potential of multimodal molecular data. Recent studies suggest that contrastive learning frameworks utilizing multimodal data exhibit excellent performance in representation learning across various domains. Building upon this, we introduce CL-MFAP, an unsupervised contrastive learning (CL)-based multimodal foundation (MF) model specifically tailored for discovering small molecules with potential antibiotic properties (AP) using three types of molecular data. This model employs 1.6 million bioactive molecules with drug-like properties from the ChEMBL dataset to jointly pretrain three encoders: (1) a transformer-based encoder with rotary position embedding for processing SMILES strings; (2) another transformer-based encoder, incorporating a novel bi-level routing attention mechanism to handle molecular graph representations; and (3) a Morgan fingerprint encoder using a multilayer perceptron, to achieve the contrastive learning purpose. The CL-MFAP outperforms baseline models in antibiotic property prediction by effectively utilizing different molecular modalities and demonstrates superior domain-specific performance when fine-tuned for antibiotic-related property prediction tasks.

Related papers

• MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation [64.4838301776267]
  Multi-channel Interaction Network (MIN) is a novel framework designed to predict drug-target interaction (DTI) MIN incorporates a representation learning module and a multi-channel interaction module. MIN is not only a potent tool for DTI prediction but also offers fresh insights into the prediction of protein binding sites.
  arXiv  Detail & Related papers  (2024-11-23T05:38:36Z)
• Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction [0.0]
  We introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization. We have established DIG-Mol's state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks.
  arXiv  Detail & Related papers  (2024-05-04T10:09:27Z)
• Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction [9.388979080270103]
  We construct multimodal deep learning models to cover different molecular representations. Compared with mono-modal models, our multimodal fused deep learning (MMFDL) models outperform single models in accuracy, reliability, and resistance capability against noise.
  arXiv  Detail & Related papers  (2023-12-29T07:19:42Z)
• Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex [5.33208055504216]
  Opioid Use Disorder (OUD) has emerged as a significant global health issue. In this study, we propose a deep generative model that combines a differential equation (SDE)-based diffusion modeling with the latent space of a pretrained autoencoder model. The molecular generator enables efficient generation of molecules that are effective on multiple targets.
  arXiv  Detail & Related papers  (2023-06-13T01:12:31Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers [9.416757363901295]
  We develop a universal random copolymer inverse design system via multi-model copolymer representation learning, knowledge distillation and reinforcement learning. By pre-training a scaffold-decorator generative model via knowledge distillation, copolymer space are greatly contracted to the near space of existing data for exploration. Our reinforcement learning algorithm can be adaptive for customized generation on specific scaffolds and requirements on property or structures.
  arXiv  Detail & Related papers  (2022-11-30T14:29:50Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization [51.00815310242277]
  generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties. We propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution.
  arXiv  Detail & Related papers  (2020-10-05T20:18:42Z)
• Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics [109.70543391923344]
  CLaSS (Controlled Latent attribute Space Sampling) is an efficient computational method for attribute-controlled generation of molecules. We screen the generated molecules for additional key attributes by using deep learning classifiers in conjunction with novel features derived from atomistic simulations. The proposed approach is demonstrated for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency.
  arXiv  Detail & Related papers  (2020-05-22T15:57:58Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

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.