Deep learning approaches for de novo drug design: An overview

2022 ◽  
Vol 72 ◽  
pp. 135-144
Author(s):  
Mingyang Wang ◽  
Zhe Wang ◽  
Huiyong Sun ◽  
Jike Wang ◽  
Chao Shen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
Learning Approaches ◽  
De Novo Drug Design

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 Applications of Autoencoders along with Deep Learning Techniques to generate valid molecules

2021 ◽  
Vol 2070 (1) ◽  
pp. 012125
Author(s):  
T Sesha Sai Aparna ◽  
T Anuradha
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
Solution Space ◽  
Threshold Value ◽  
Activation Function ◽  
Generative Models ◽  
Pharmaceutical Chemical ◽  
De Novo Drug Design

Abstract From the moment of identifying the fundamental cause of an illness to its availability in the marketplace, it takes an average of 10 years and almost $2.6 billion dollars to develop a medication. We’re actually hunting for a needle in a haystack, which takes a lot of time, effort, and money. In a solution space of between 1030 and 10100 synthetically viable compounds, we’re seeking for the one molecule that can turn off a disease at the molecular level. The chemical solution space is just too large to adequately screen for the desired molecule. Only a small percentage of the synthetically viable compounds for wet lab research are stored in pharmaceutical chemical repositories. Computational de novo drug design can be used to explore this vast chemical space and develop previously undesigned compounds. Computational drug design can cut the amount of time spent in the discovery phase in half, resulting in a shorter time to market and lower drug prices. Deep learning and artificial intelligence (AI) have opened up new perspectives in cheminformatics, especially in molecules generative models. Recurrent neural networks (RNNs) trained with molecules in the SMILES text format, in particular, are very good at exploring the chemical space. Two baseline models were created for generating molecules, one of the model includes an encoder that takes SMILES as input and then develops a deep generative LSTM model which acts as a hidden layer and the output from layers acts as an input to the decoder. The other baseline model acts the same as the above-mentioned model but it includes latent space, it is simply a representation of compressed data that bring related data points closer together physically. To learn data properties and find simpler data representations for analysis, and weights which are obtained from the previous model to generate more efficient molecules. Then created a custom function to play with the temperature of the softmax activation function which creates a threshold value for the valid molecules to generate. This model enables us to produce new molecules through successful exploration.

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

Deep learning for de novo drug design

2019 ◽  
Author(s):  
R Winter ◽  
F Montanari ◽  
DA Clevert
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
De Novo Drug Design

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.

Lead Molecules as Novel Aromatase Inhibitors: In Silico De Novo Designing and Binding Affinity Studies

2020 ◽  
Vol 17 (5) ◽  
pp. 655-665 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Drug Design ◽  
Aromatase Inhibitors ◽  
De Novo ◽  
Scale Up ◽  
Geometry Optimization ◽  
Docking Study ◽  
Molecular Docking Study ◽  
De Novo Drug Design ◽  
Molegro Virtual Docker ◽  
Estrogen Production

Background:Aromatase inhibitors emerged as a pivotal moiety to selectively block estrogen production, prevention and treatment of tumour growth in breast cancer. De novo drug design is an alternative approach to blind virtual screening for successful designing of the novel molecule against various therapeutic targets.Objective:In the present study, we have explored the de novo approach to design novel aromatase inhibitors.Method:The e-LEA3D, a computational-aided drug design web server was used to design novel drug-like candidates against the target aromatase. For drug-likeness ADME parameters (molecular weight, H-bond acceptors, H-bond donors, LogP and number of rotatable bonds) of designed molecules were calculated in TSAR software package, geometry optimization and energy minimization was accomplished using Chem Office. Further, molecular docking study was performed in Molegro Virtual Docker (MVD).Results:Among 17 generated molecules using the de novo pathway, 13 molecules passed the Lipinski filter pertaining to their bioavailability characteristics. De novo designed molecules with drug-likeness were further docked into the mapped active site of aromatase to scale up their affinity and binding fitness with the target. Among de novo fabricated drug like candidates (1-13), two molecules (5, 6) exhibited higher affinity with aromatase in terms of MolDock score (-150.650, -172.680 Kcal/mol, respectively) while molecule 8 showed lowest target affinity (-85.588 Kcal/mol).Conclusion:The binding patterns of lead molecules (5, 6) could be used as a pharmacophore for medicinal chemists to explore these molecules for their aromatase inhibitory potential.

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