Graph Energy-based Model for Substructure Preserving Molecular Design
- URL: http://arxiv.org/abs/2102.04600v1
- Date: Tue, 9 Feb 2021 01:46:12 GMT
- Title: Graph Energy-based Model for Substructure Preserving Molecular Design
- Authors: Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe
- Abstract summary: Our Graph Energy-based Model, or GEM, can fix substructures and generate the rest.
The experimental results show that the GEMs trained from chemistry datasets successfully generate novel molecules.
- Score: 15.939981475281309
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is common practice for chemists to search chemical databases based on
substructures of compounds for finding molecules with desired properties. The
purpose of de novo molecular generation is to generate instead of search.
Existing machine learning based molecular design methods have no or limited
ability in generating novel molecules that preserves a target substructure. Our
Graph Energy-based Model, or GEM, can fix substructures and generate the rest.
The experimental results show that the GEMs trained from chemistry datasets
successfully generate novel molecules while preserving the target
substructures. This method would provide a new way of incorporating the domain
knowledge of chemists in molecular design.
Related papers
- GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned.
We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
arXiv Detail & Related papers (2024-11-03T19:45:15Z) - Molecule Design by Latent Space Energy-Based Modeling and Gradual
Distribution Shifting [53.44684898432997]
Generation of molecules with desired chemical and biological properties is critical for drug discovery.
We propose a probabilistic generative model to capture the joint distribution of molecules and their properties.
Our method achieves very strong performances on various molecule design tasks.
arXiv Detail & Related papers (2023-06-09T03:04:21Z) - An Equivariant Generative Framework for Molecular Graph-Structure
Co-Design [54.92529253182004]
We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design.
In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure.
Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
arXiv Detail & Related papers (2023-04-12T13:34:22Z) - 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) - 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) - 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) - Inverse design of 3d molecular structures with conditional generative
neural networks [2.7998963147546148]
We propose a conditional generative neural network for 3d molecular structures with specified structural and chemical properties.
This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions.
arXiv Detail & Related papers (2021-09-10T12:12:38Z) - Learning Latent Space Energy-Based Prior Model for Molecule Generation [59.875533935578375]
We learn latent space energy-based prior model with SMILES representation for molecule modeling.
Our method is able to generate molecules with validity and uniqueness competitive with state-of-the-art models.
arXiv Detail & Related papers (2020-10-19T09:34:20Z) - Reinforcement Learning for Molecular Design Guided by Quantum Mechanics [10.112779201155005]
We present a novel RL formulation for molecular design in coordinates, thereby extending the class of molecules that can be built.
Our reward function is directly based on fundamental physical properties such as the energy, which we approximate via fast quantum-chemical methods.
In our experiments, we show that our agent can efficiently learn to solve these tasks from scratch by working in a translation and rotation invariant state-action space.
arXiv Detail & Related papers (2020-02-18T16:43:58Z)
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.