ControlMol: Adding Substructure Control To Molecule Diffusion Models
- URL: http://arxiv.org/abs/2405.06659v2
- Date: Sat, 21 Dec 2024 08:22:54 GMT
- Title: ControlMol: Adding Substructure Control To Molecule Diffusion Models
- Authors: Qi Zhengyang, Liu Zijing, Zhang Jiying, Cao He, Li Yu,
- Abstract summary: We propose a two-stage training approach, consisting of condition learning and condition optimization.
In our experiments, only trained on randomly partitioned sub-structure data, the proposed method outperforms previous techniques by generating more valid and diverse molecules.
- Score: 2.8372258697984627
- License:
- Abstract: Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. Existing works mainly focus on specific tasks, such as linker design or scaffold hopping, each task requires training a model from scratch, and many well-pretrained De Novo molecule generation model parameters are not effectively utilized. To this end, we propose a two-stage training approach, consisting of condition learning and condition optimization. In the condition learning stage, we adopt the idea of ControlNet and design some meaningful adjustments to make the unconditional generative model learn sub-structure conditioned generation. In the condition optimization stage, by using human preference learning, we further enhance the stability and robustness of sub-structure control. In our experiments, only trained on randomly partitioned sub-structure data, the proposed method outperforms previous techniques by generating more valid and diverse molecules. Our method is easy to implement and can be quickly applied to various pre-trained molecule generation models.
Related papers
- Unified Guidance for Geometry-Conditioned Molecular Generation [41.94578826467316]
We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models.
We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework.
arXiv Detail & Related papers (2025-01-05T12:58:01Z) - Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.
We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)
Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
arXiv Detail & Related papers (2024-10-17T00:35:47Z) - 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) - DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization [49.85944390503957]
DecompOpt is a structure-based molecular optimization method based on a controllable and diffusion model.
We show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines.
arXiv Detail & Related papers (2024-03-07T02:53:40Z) - Bidirectional Generation of Structure and Properties Through a Single
Molecular Foundation Model [44.60174246341653]
We present a novel multimodal molecular pre-trained model that incorporates the modalities of structure and biochemical properties.
Our proposed model pipeline of data handling and training objectives aligns the structure/property features in a common embedding space.
These contributions emerge synergistic knowledge, allowing us to tackle both multimodal and unimodal downstream tasks through a single model.
arXiv Detail & Related papers (2022-11-19T05:16:08Z) - 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) - Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data.
Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
arXiv Detail & Related papers (2021-11-30T06:10:22Z) - Learning Multi-Objective Curricula for Deep Reinforcement Learning [55.27879754113767]
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL)
In this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula.
In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum.
arXiv Detail & Related papers (2021-10-06T19:30:25Z) - Learning Neural Generative Dynamics for Molecular Conformation
Generation [89.03173504444415]
We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph.
We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
arXiv Detail & Related papers (2021-02-20T03:17:58Z)
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.