Chemistry42: An AI-based platform for de novo molecular design
- URL: http://arxiv.org/abs/2101.09050v1
- Date: Fri, 22 Jan 2021 10:49:26 GMT
- Title: Chemistry42: An AI-based platform for de novo molecular design
- Authors: Yan A. Ivanenkov, Alex Zhebrak, Dmitry Bezrukov, Bogdan Zagribelnyy,
Vladimir Aladinskiy, Daniil Polykovskiy, Evgeny Putin, Petrina Kamya,
Alexander Aliper, Alex Zhavoronkov
- Abstract summary: Chemistry42 is a software platform for de novo small molecule design.
It integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methods.
- Score: 48.40662244096031
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Chemistry42 is a software platform for de novo small molecule design that
integrates Artificial Intelligence (AI) techniques with computational and
medicinal chemistry methods. Chemistry42 is unique in its ability to generate
novel molecular structures with predefined properties validated through in
vitro and in vivo studies. Chemistry42 is a core component of Insilico Medicine
Pharma.ai drug discovery suite that also includes target discovery and
multi-omics data analysis (PandaOmics) and clinical trial outcomes predictions
(InClinico).
Related papers
- Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning [0.0]
We introduce an innovative de-novo drug design strategy, which harnesses the capabilities of language models to devise targeted drugs for specific proteins.
Our method integrates a composite reward function, combining considerations of drug-target interaction and molecular validity.
arXiv Detail & Related papers (2024-05-10T22:19:12Z) - Interactive Molecular Discovery with Natural Language [69.89287960545903]
We propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules.
To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information.
arXiv Detail & Related papers (2023-06-21T02:05:48Z) - QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories.
We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
arXiv Detail & Related papers (2023-06-15T23:39:07Z) - 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) - 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) - Semi-Supervised GCN for learning Molecular Structure-Activity
Relationships [4.468952886990851]
We propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships.
As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design.
arXiv Detail & Related papers (2022-01-25T09:09:43Z) - AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery:
Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small
Molecule Inhibitor [9.89420507558956]
We successfully applied AlphaFold to identify a first-in-class hit molecule of a novel target without an experimental structure.
We identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM within 30 days from target selection and after only 7 compounds.
This is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.
arXiv Detail & Related papers (2022-01-21T07:35:24Z) - Graph Energy-based Model for Substructure Preserving Molecular Design [15.939981475281309]
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.
arXiv Detail & Related papers (2021-02-09T01:46:12Z) - Advanced Graph and Sequence Neural Networks for Molecular Property
Prediction and Drug Discovery [53.00288162642151]
We develop MoleculeKit, a suite of comprehensive machine learning tools spanning different computational models and molecular representations.
Built on these representations, MoleculeKit includes both deep learning and traditional machine learning methods for graph and sequence data.
Results on both online and offline antibiotics discovery and molecular property prediction tasks show that MoleculeKit achieves consistent improvements over prior methods.
arXiv Detail & Related papers (2020-12-02T02:09:31Z)
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.