scholarly journals Structure-based de novo drug design using 3D deep generative models

2021 ◽  
Author(s):  
Yibo Li ◽  
Jianfeng Pei ◽  
Luhua Lai
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
De Novo ◽  
Generative Models ◽  
Generative Model ◽  
De Novo Drug Design ◽  
3D Structures ◽  
Target Binding ◽  
Target Binding Site

DeepLigBuilder, a novel deep generative model for structure-based de novo drug design, directly generates 3D structures of drug-like compounds in the target binding site.

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.

Generative Models for De Novo Drug Design

2021 ◽  
Author(s):  
Xiaochu Tong ◽  
Xiaohong Liu ◽  
Xiaoqin Tan ◽  
Xutong Li ◽  
Jiaxin Jiang ◽  
...  
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Generative Models ◽  
De Novo Drug Design

scholarly journals De novo drug design using reinforcement learning with graph-based deep generative models

2021 ◽  
Author(s):  
Sara Romeo Atance ◽  
Juan Viguera Diez ◽  
Ola Engkvist ◽  
Simon Olsson ◽  
Rocío Mercado
Keyword(s):  
Reinforcement Learning ◽  
Drug Design ◽  
De Novo ◽  
Generative Models ◽  
De Novo Drug Design

Graph-based generative models for de Novo drug design

2019 ◽  
Vol 32-33 ◽  
pp. 45-53 ◽  
Author(s):  
Xiaolin Xia ◽  
Jianxing Hu ◽  
Yanxing Wang ◽  
Liangren Zhang ◽  
Zhenming Liu
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Generative Models ◽  
De Novo Drug Design

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.

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

A computational study on cannabinoid receptors and potent bioactive cannabinoid ligands: homology modeling, docking, de novo drug design and molecular dynamics analysis

2009 ◽  
Vol 14 (2) ◽  
pp. 257-276 ◽  
Author(s):  
Serdar Durdagi ◽  
Manthos G. Papadopoulos ◽  
Panagiotis G. Zoumpoulakis ◽  
Catherine Koukoulitsa ◽  
Thomas Mavromoustakos
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Homology Modeling ◽  
Cannabinoid Receptors ◽  
De Novo ◽  
Computational Study ◽  
Dynamics Analysis ◽  
De Novo Drug Design

scholarly journals SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design

2020 ◽  
Author(s):  
Josep Arús-Pous ◽  
Atanas Patronov ◽  
Esben Jannik Bjerrum ◽  
Christian Tyrchan ◽  
Jean-Louis Reymond ◽  
...  
Keyword(s):  
Data Augmentation ◽  
De Novo ◽  
Chemical Space ◽  
Generative Models ◽  
Synthetic Chemistry ◽  
Specific Knowledge ◽  
De Novo Drug Design ◽  
Wide Range ◽  
Small Set ◽  
Synthetic Approaches

Molecular generative models trained with small sets of molecules represented as SMILES strings are able to generate large regions of the chemical space. Unfortunately, due to the sequential nature of SMILES strings, these models are not able to generate molecules given a scaffold (i.e. partially-built molecules with explicit attachment points). Herein we report a new SMILES-based molecular generative architecture that generates molecules from scaffolds and can be trained from any arbitrary molecular set. This is possible thanks to a new molecular set pre-processing algorithm that exhaustively cuts all combinations of acyclic bonds of every molecule, obtaining a large number of scaffold-decorations combinations. Moreover, it serves as a data augmentation technique and can be readily coupled with randomized SMILES to obtain even better results with small sets. Two examples showcasing the potential of the architecture in medicinal and synthetic chemistry are described: First, models were trained with a training set obtained from a small set of Dopamine Receptor D2 (DRD2) active modulators and were able to meaningfully decorate a wide range of scaffolds and obtain molecular series predicted active on DRD2. Second, a larger set of drug-like molecules from ChEMBL was selectively sliced using synthetic chemistry constraints (RECAP rules). Moreover, the resulting scaffold-decorations were filtered to only allow decorations that were fragment-like. This allowed models trained with this dataset to selectively decorate diverse scaffolds with fragments that were generally predicted to be synthesizable and attachable to the scaffold using known synthetic approaches. In both cases, the models were already able to decorate molecules using specific knowledge without the need to add it with other techniques, such as reinforcement learning. We envision that this architecture will become a useful addition to the already existent architectures for de-novo molecular generation.

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