When Molecular GAN Meets Byte-Pair Encoding

• URL: http://arxiv.org/abs/2409.19740v1
• Date: Sun, 29 Sep 2024 15:39:26 GMT
• Title: When Molecular GAN Meets Byte-Pair Encoding
• Authors: Huidong Tang, Chen Li, Yasuhiko Morimoto,
• Abstract summary: This study introduces a molecular GAN that integrates a byte level byte-pair encoding tokenizer and employs reinforcement learning to enhance de novo molecular generation. Specifically, the generator functions as an actor, producing SMILES strings, while the discriminator acts as a critic, evaluating their quality.
• Score: 2.5398391570038736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep generative models, such as generative adversarial networks (GANs), are pivotal in discovering novel drug-like candidates via de novo molecular generation. However, traditional character-wise tokenizers often struggle with identifying novel and complex sub-structures in molecular data. In contrast, alternative tokenization methods have demonstrated superior performance. This study introduces a molecular GAN that integrates a byte level byte-pair encoding tokenizer and employs reinforcement learning to enhance de novo molecular generation. Specifically, the generator functions as an actor, producing SMILES strings, while the discriminator acts as a critic, evaluating their quality. Our molecular GAN also integrates innovative reward mechanisms aimed at improving computational efficiency. Experimental results assessing validity, uniqueness, novelty, and diversity, complemented by detailed visualization analysis, robustly demonstrate the effectiveness of our GAN.

Related papers

• FARM: Functional Group-Aware Representations for Small Molecules [55.281754551202326]
  We introduce Functional Group-Aware Representations for Small Molecules (FARM) FARM is a foundation model designed to bridge the gap between SMILES, natural language, and molecular graphs. We rigorously evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 10 out of 12 tasks.
  arXiv  Detail & Related papers  (2024-10-02T23:04:58Z)
• Crossing New Frontiers: Knowledge-Augmented Large Language Model Prompting for Zero-Shot Text-Based De Novo Molecule Design [0.0]
  Our study explores the use of knowledge-augmented prompting of large language models (LLMs) for the zero-shot text-conditional de novo molecular generation task. Our framework proves effective, outperforming state-of-the-art (SOTA) baseline models on benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-18T11:37:19Z)
• 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)
• Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation [0.0]
  Mol-AIR is a reinforcement learning-based framework using adaptive intrinsic rewards for goal-directed molecular generation. In benchmark tests, Mol-AIR demonstrates superior performance over existing approaches in generating molecules with desired properties.
  arXiv  Detail & Related papers  (2024-03-29T10:44:51Z)
• Molecular Generative Adversarial Network with Multi-Property Optimization [3.0001188337985236]
  Deep generative models, such as generative adversarial networks (GANs), have been employed for $denovo$ molecular generation in drug discovery. This study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization.
  arXiv  Detail & Related papers  (2024-03-29T08:55:39Z)
• Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry [6.049566024728809]
  Deep learning-based molecular property prediction has emerged as a solution to the resource-intensive nature of traditional methods. In this paper, we propose a novel multi-modal representation learning model, called SGGRL, for molecular property prediction. To ensure consistency across modalities, SGGRL is trained to maximize the similarity of representations for the same molecule while minimizing similarity for different molecules.
  arXiv  Detail & Related papers  (2024-01-07T02:18:00Z)
• 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)
• MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning [77.31492888819935]
  We propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT) MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. Experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction.
  arXiv  Detail & Related papers  (2022-12-20T19:32:30Z)
• Retrieval-based Controllable Molecule Generation [63.44583084888342]
  We propose a new retrieval-based framework for controllable molecule generation. We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
  arXiv  Detail & Related papers  (2022-08-23T17:01:16Z)
• Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge [1.4410716345002657]
  generative machine learning techniques can generate novel chemical structures based on molecular fingerprint representation. Lack of supervised domain knowledge can mislead the learning procedure to be relatively biased to the prevalent molecules observed in the training data. We alleviated this drawback by augmenting the training data with domain knowledge, e.g. quantitative estimates of the drug-likeness score (QEDs)
  arXiv  Detail & Related papers  (2022-04-05T20:16:11Z)
• Reinforced Molecular Optimization with Neighborhood-Controlled Grammars [63.84003497770347]
  We propose MNCE-RL, a graph convolutional policy network for molecular optimization. We extend the original neighborhood-controlled embedding grammars to make them applicable to molecular graph generation. We show that our approach achieves state-of-the-art performance in a diverse range of molecular optimization tasks.
  arXiv  Detail & Related papers  (2020-11-14T05:42:15Z)

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.