nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder

• URL: http://arxiv.org/abs/2410.09240v1
• Date: Fri, 11 Oct 2024 20:30:44 GMT
• Title: nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder
• Authors: Maksim Kuznetsov, Airat Valiev, Alex Aliper, Daniil Polykovskiy, Elena Tutubalina, Rim Shayakhmetov, Zulfat Miftahutdinov,
• Abstract summary: nach0-pc is a model combining domain-specific encoder and textual representation to handle spatial arrangement of atoms effectively. We introduce a novel pre-training scheme for molecular point clouds to distillate the knowledge from spatial molecular structures datasets.
• Score: 8.125243669908318
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements have integrated Language Models (LMs) into a drug discovery pipeline. However, existing models mostly work with SMILES and SELFIES chemical string representations, which lack spatial features vital for drug discovery. Additionally, attempts to translate chemical 3D structures into text format encounter issues such as excessive length and insufficient atom connectivity information. To address these issues, we introduce nach0-pc, a model combining domain-specific encoder and textual representation to handle spatial arrangement of atoms effectively. Our approach utilizes a molecular point cloud encoder for concise and order-invariant structure representation. We introduce a novel pre-training scheme for molecular point clouds to distillate the knowledge from spatial molecular structures datasets. After fine-tuning within both single-task and multi-task frameworks, nach0-pc demonstrates performance comparable with other diffusion models in terms of generated samples quality across several established spatial molecular generation tasks. Notably, our model is a multi-task approach, in contrast to diffusion models being limited to single tasks. Additionally, it is capable of processing point cloud-related data, which language models are not capable of handling due to memory limitations. These lead to our model having reduced training and inference time while maintaining on par performance.

Related papers

• Exploring Discrete Flow Matching for 3D De Novo Molecule Generation [0.0]
  Flow matching is a recently proposed generative modeling framework that has achieved impressive performance on a variety of tasks. We present FlowMol-CTMC, an open-source model that achieves state of the art performance for 3D de novo design with fewer learnable parameters than existing methods.
  arXiv  Detail & Related papers  (2024-11-25T18:27:39Z)
• Pre-trained Molecular Language Models with Random Functional Group Masking [54.900360309677794]
  We propose a SMILES-based underlineem Molecular underlineem Language underlineem Model, which randomly masking SMILES subsequences corresponding to specific molecular atoms. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities.
  arXiv  Detail & Related papers  (2024-11-03T01:56:15Z)
• Generative Modeling of Molecular Dynamics Trajectories [12.255021091552441]
  We introduce generative modeling of molecular trajectories as a paradigm for learning flexible multi-task surrogate models of MD from data. We show such generative models can be adapted to diverse tasks such as forward simulation, transition path sampling, and trajectory upsampling.
  arXiv  Detail & Related papers  (2024-09-26T13:02:28Z)
• Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models [0.0]
  We systematically evaluate thirteen chemistry-specific tokenizers for their coverage of the SMILES language. We introduce two new tokenizers, i>smirk/i> and i>smirk-gpe/i>, which can represent the entirety of the OpenSMILES specification.
  arXiv  Detail & Related papers  (2024-09-19T02:36:04Z)
• 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)
• 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)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning [71.89623260998934]
  This study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. Existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. We propose GIMLET, which unifies language models for both graph and text data.
  arXiv  Detail & Related papers  (2023-05-28T18:27:59Z)
• 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)
• Reprogramming Language Models for Molecular Representation Learning [65.00999660425731]
  We propose Representation Reprogramming via Dictionary Learning (R2DL) for adversarially reprogramming pretrained language models for molecular learning tasks. The adversarial program learns a linear transformation between a dense source model input space (language data) and a sparse target model input space (e.g., chemical and biological molecule data) using a k-SVD solver. R2DL achieves the baseline established by state of the art toxicity prediction models trained on domain-specific data and outperforms the baseline in a limited training-data setting.
  arXiv  Detail & Related papers  (2020-12-07T05:50:27Z)
• Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics [21.95240820041655]
  Variational Autos (VAEs) are generative models in which encoder-decoder network pairs are trained to reconstruct training data distributions. We propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features.
  arXiv  Detail & Related papers  (2020-10-18T13:33:02Z)

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.