scholarly journals De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

BioChem ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 36-48
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Natural Product ◽  
De Novo ◽  
Viral Proteins ◽  
Computer Assisted ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Latent Space ◽  
Synthetic Molecule

Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.

scholarly journals DenovoProfiling: a webserver for de novo generated molecule library profiling

2021 ◽  
Author(s):  
Zhihong Liu ◽  
Jiewen Du ◽  
Bingdong Liu ◽  
Zongbin Cui ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Drug Discovery Process ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Functional Profiling ◽  
De Novo Molecular Design ◽  
Selection Of

Abstract With the advances of deep learning techniques, various architectures for molecular generation have been proposed for de novo drug design. Successful cases from academia and industrial demonstrated that the deep learning-based de novo molecular design could efficiently accelerate the drug discovery process. The flourish of the de novo molecular generation methods and applications created a great demand for the visualization and functional profiling for the de novo generated molecules. The rising of publicly available chemogenomic databases lays good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization, (2) chemical space, (3) scaffold analysis, (4) molecular alignment, (5) drugs mapping, and (6) target & pathway. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

scholarly journals DenovoProfiling: a webserver for de novo generated molecule library profiling

2021 ◽  
Author(s):  
Zhihong Liu ◽  
Jiewen Du ◽  
Bingdong Liu ◽  
Zongbin Cui ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Drug Discovery Process ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Functional Profiling ◽  
De Novo Molecular Design ◽  
Selection Of

AbstractWith the advances of deep learning techniques, various architectures for molecular generation have been proposed for de novo drug design. Successful cases from academia and industrial demonstrated that the deep learning based de novo molecular design could efficiently accelerate the drug discovery process. The flourish of the de novo molecular generation methods and applications created great demand for the visualization and functional profiling for the de novo generated molecules. The rising of publicly available chemogenomic databases lays good foundations and create good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a web server dedicated for de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization, (2) chemical space, (3) scaffold analysis, (4) molecular alignment, (5) target & pathways, and (6) drugs mapping. DenovoProfiling could provide structural identification, chemical space exploration, drugs mapping, and targets & pathways. The comprehensive annotated information could give user a clear picture of their de novo library and could provide guidance in the further selection of candidates for synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

scholarly journals DenovoProfiling: A Webserver for De Novo Generated Molecule Library Profiling

2021 ◽  
Author(s):  
Zhihong Liu ◽  
Jiewen Du ◽  
Bingdong Liu ◽  
Zongbin Cui ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Drug Discovery Process ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Functional Profiling ◽  
De Novo Molecular Design ◽  
Selection Of

Abstract With the advances of deep learning techniques, various architectures for molecular generation have been proposed for de novo drug design. Successful cases from academia and industrial demonstrated that the deep learning based de novo molecular design could efficiently accelerate the drug discovery process. The flourish of the de novo molecular generation methods and applications created great demand for the visualization and functional profiling for the de novo generated molecules. The rising of publicly available chemogenomic databases lays good foundations and create good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a web server dedicated for de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization, (2) chemical space, (3) scaffold analysis, (4) molecular alignment, (5) target & pathways, and (6) drugs mapping. DenovoProfiling could provide structural identification, chemical space exploration, drugs mapping, and targets & pathways. The comprehensive annotated information could give user a clear picture of their de novo library and could provide guidance in the further selection of candidates for synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

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 Yuel: Compound-Protein Interaction Prediction with High Generalizability

2021 ◽  
Author(s):  
Jian Wang ◽  
Nikolay V Dokholyan
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Small Molecules ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Data Sets ◽  
Interaction Prediction ◽  
Learning Techniques ◽  
Protein Interaction Prediction ◽  
High Prediction

In recent years, numerous structure-free deep-learning-based neural networks have emerged aiming to predict compound-protein interactions for drug virtual screening. Although these methods show high prediction accuracy in their own tests, we find that they are not generalizable to predict interactions between unknown proteins and unknown small molecules, thus hindering the utilization of state-of-the-art deep learning techniques in the field of virtual screening. In our work, we develop a compound-protein interaction predictor, YueL, which can predict compound-protein interactions with high generalizability. Upon comprehensive tests on various data sets, we find that YueL has the ability to predict interactions between unknown compounds and unknown proteins. We anticipate our work can motivate broad application of deep learning techniques for drug virtual screening to supersede the traditional docking and cheminformatics methods.

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 De Novo Drug Design using Artificial Intelligence ASYNT-GAN

2020 ◽  
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Molecular Structure ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
De Novo ◽  
Protein Structures ◽  
Generative Adversarial Networks ◽  
Text Generation ◽  
De Novo Drug Design ◽  
3D Space ◽  
Adversarial Networks

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

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.

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