Target Specific De Novo Design of Drug Candidate Molecules with Graph
Transformer-based Generative Adversarial Networks
- URL: http://arxiv.org/abs/2302.07868v3
- Date: Fri, 17 Feb 2023 17:33:55 GMT
- Title: Target Specific De Novo Design of Drug Candidate Molecules with Graph
Transformer-based Generative Adversarial Networks
- Authors: Atabey \"Unl\"u, Elif \c{C}evrim, Ahmet Sar{\i}g\"un, Hayriye
\c{C}elikbilek, Heval Ata\c{s} G\"uvenilir, Altay Koya\c{s}, Deniz Cansen
Kahraman, Abdurrahman Ol\u{g}a\c{c}, Ahmet Rifaio\u{g}lu, Tunca Do\u{g}an
- Abstract summary: We propose DrugGEN, for the de novo design of drug candidate molecules that interact with selected target proteins.
DrugGEN is trained using a large dataset of compounds from ChEMBL and target-specific bioactive molecules.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Discovering novel drug candidate molecules is one of the most fundamental and
critical steps in drug development. Generative deep learning models, which
create synthetic data given a probability distribution, have been developed
with the purpose of picking completely new samples from a partially known
space. Generative models offer high potential for designing de novo molecules;
however, in order for them to be useful in real-life drug development
pipelines, these models should be able to design target-specific molecules,
which is the next step in this field. In this study, we propose DrugGEN, for
the de novo design of drug candidate molecules that interact with selected
target proteins. The proposed system represents compounds and protein
structures as graphs and processes them via serially connected two generative
adversarial networks comprising graph transformers. DrugGEN is trained using a
large dataset of compounds from ChEMBL and target-specific bioactive molecules,
to design effective and specific inhibitory molecules against the AKT1 protein,
which has critical importance for developing treatments against various types
of cancer. On fundamental benchmarks, DrugGEN models have either competitive or
better performance against other methods. To assess the target-specific
generation performance, we conducted further in silico analysis with molecular
docking and deep learning-based bioactivity prediction. Results indicate that
de novo molecules have high potential for interacting with the AKT1 protein
structure in the level of its native ligand. DrugGEN can be used to design
completely novel and effective target-specific drug candidate molecules for any
druggable protein, given target features and a dataset of experimental
bioactivities. Code base, datasets, results and trained models of DrugGEN are
available at https://github.com/HUBioDataLab/DrugGEN
Related papers
- SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction
and Drug Design [64.69434941796904]
We propose a novel setting and models for in-context drug synergy learning.
We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.
Our goal is to predict additional drug synergy relationships in that context.
arXiv Detail & Related papers (2023-06-19T17:03:46Z) - Drug Synergistic Combinations Predictions via Large-Scale Pre-Training
and Graph Structure Learning [82.93806087715507]
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation.
Deep learning models have emerged as an efficient way to discover synergistic combinations.
Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
arXiv Detail & Related papers (2023-01-14T15:07:43Z) - Energy-based Generative Models for Target-specific Drug Discovery [7.509129971169722]
We develop an energy-based probabilistic model for computational target-specific drug discovery.
Results show that our proposed TagMol can generate molecules with similar binding affinity scores as real molecules.
arXiv Detail & Related papers (2022-12-05T16:41:36Z) - Drug-target affinity prediction method based on consistent expression of
heterogeneous data [0.0]
We propose a method for predicting drug-target binding affinity using deep learning models.
The proposed model demonstrates its accuracy and effectiveness in predicting drug-target binding affinity on the DAVIS and KIBA datasets.
arXiv Detail & Related papers (2022-11-13T02:58:03Z) - Exploiting Pretrained Biochemical Language Models for Targeted Drug
Design [0.1889930012459365]
We propose exploiting pretrained biochemical language models to initialize targeted molecule generation models.
We compare two decoding strategies to generate compounds: beam search and sampling.
arXiv Detail & Related papers (2022-09-02T12:21:51Z) - Tailoring Molecules for Protein Pockets: a Transformer-based Generative
Solution for Structured-based Drug Design [133.1268990638971]
De novo drug design based on the structure of a target protein can provide novel drug candidates.
We present a generative solution named TamGent that can directly generate candidate drugs from scratch for a given target.
arXiv Detail & Related papers (2022-08-30T09:32:39Z) - 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) - Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based
Reinforcement Learning Model [4.815696666006742]
Structure-based de novo method can overcome the data scarcity of active by incorporating drug-target interaction into deep generative architectures.
Here, we demonstrate a widely used and fast protein sequence-based reinforcement learning model for drug discovery.
As a proof of concept, the RL model was utilized to design molecules for four targets.
arXiv Detail & Related papers (2022-08-14T10:41:52Z) - Improved Drug-target Interaction Prediction with Intermolecular Graph
Transformer [98.8319016075089]
We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture.
Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively.
IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
arXiv Detail & Related papers (2021-10-14T13:28:02Z) - Benchmarking Deep Graph Generative Models for Optimizing New Drug
Molecules for COVID-19 [11.853524110656991]
Design of new drug compounds with target properties is a key area of research in generative modeling.
We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates.
arXiv Detail & Related papers (2021-02-09T17:49:26Z) - CogMol: Target-Specific and Selective Drug Design for COVID-19 Using
Deep Generative Models [74.58583689523999]
We propose an end-to-end framework, named CogMol, for designing new drug-like small molecules targeting novel viral proteins.
CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme.
CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity.
arXiv Detail & Related papers (2020-04-02T18:17:20Z)
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.