De Novo Structure-Based Drug Design Using Deep Learning

2021 ◽  
Author(s):  
Sowmya Ramaswamy Krishnan ◽  
Navneet Bung ◽  
Sarveswara Rao Vangala ◽  
Rajgopal Srinivasan ◽  
Gopalakrishnan Bulusu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
Structure Based Drug Design

scholarly journals Target2DeNovoDrug : a novel programmatic tool for deep learning based de novo drug design for a target of interest

2020 ◽  
Author(s):  
Rafal Madaj ◽  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
In Silico ◽  
High Performance ◽  
Data Science ◽  
De Novo ◽  
Computationally Efficient ◽  
De Novo Drug Design ◽  
Necrosis Factor Alpha ◽  
Structure Based Drug Design

The on-going data-science and AI revolution offers researchers with fresh set of tools to approach structure-based drug design problems in the computer aided drug design space. A novel programmatic tool that can be used in aid of in silico-deep learning based de novo drug design for any target of interest has been reported. Once the user specifies the target of interest, the programmatic workflow of the tool generates novel SMILES of compounds that are likely to be active against the target. The tool also performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results in the working folder of the user. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained and supported at the GitHub repository given in link below https://github.com/bengeof/Target2DeNovoDrug

scholarly journals A Structure-Based Drug Discovery Paradigm

2019 ◽  
Vol 20 (11) ◽  
pp. 2783 ◽  
Author(s):  
Maria Batool ◽  
Bilal Ahmad ◽  
Sangdun Choi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Learning Tools ◽  
Statistical Machine Learning ◽  
Lead Discovery ◽  
De Novo Drug Design ◽  
Structure Based Drug Design

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning

2021 ◽  
Vol 61 (2) ◽  
pp. 621-630
Author(s):  
Sowmya Ramaswamy Krishnan ◽  
Navneet Bung ◽  
Gopalakrishnan Bulusu ◽  
Arijit Roy
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
De Novo Drug Design ◽  
Novel Proteins

scholarly journals DrugEx v3: Scaffold-Constrained Drug Design with Graph Transformer-based Reinforcement Learning

2021 ◽  
Author(s):  
Xuhan Liu ◽  
Kai Ye ◽  
Herman W. T. van Vlijmen ◽  
Adriaan P. IJzerman ◽  
Gerard J. P. van Westen
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Molecular Structures ◽  
Rational Drug Design ◽  
Graph Representation ◽  
General Applicability ◽  
De Novo Drug Design

Due to the large drug-like chemical space available to search for feasible drug-like molecules, rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified. With the rapid growth of the application of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work, we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives similar to other known methods and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. In this work, the Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules we proposed a novel positional encoding for each atom and bond based on an adjacency matrix to extend the architecture of the Transformer. Each molecule was generated by growing and connecting procedures for the fragments in the given scaffold that were unified into one model. Moreover, we trained this generator under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, our proposed method was applied to design ligands for the adenosine A2A receptor (A2AAR) and compared with SMILES-based methods. The results demonstrated the effectiveness of our method in that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A2AAR with given scaffolds.

scholarly journals Comprehensive Survey of Recent Drug Discovery Using Deep Learning

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Author(s):  
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Molecular Structures ◽  
De Novo Drug Design ◽  
Related Data ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

scholarly journals Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3250 ◽  
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Protein Design ◽  
De Novo ◽  
De Novo Design ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Discovery Research ◽  
De Novo Peptide

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

Towards structure-based protein drug design

2011 ◽  
Vol 39 (5) ◽  
pp. 1382-1386 ◽  
Author(s):  
Changsheng Zhang ◽  
Luhua Lai
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Small Molecule ◽  
Protein Interactions ◽  
De Novo ◽  
Protein Docking ◽  
Erythropoietin Receptor ◽  
Protein Drug ◽  
Protein Protein Interactions ◽  
Structure Based Drug Design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein–protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein–protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.

Sign in / Sign up

Export Citation Format

Share Document