scholarly journals WADDAICA: a webserver for aiding protein drug design by artificial intelligence and classical algorithm

2021 ◽  
Author(s):  
Qifeng Bai ◽  
Jian Ma ◽  
Shuo Liu ◽  
Tingyang Xu ◽  
Antonio Jesús Banegas-Luna ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Design ◽  
Protein Drug ◽  
Classical Algorithm

scholarly journals MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

2020 ◽  
Author(s):  
Qifeng Bai ◽  
Shuoyan Tan ◽  
Tingyang Xu ◽  
Huanxiang Liu ◽  
Junzhou Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Drug Design ◽  
Membrane Protein ◽  
Learning Model ◽  
Glucagon Receptor ◽  
Protein Targets ◽  
Lipinski’S Rule ◽  
Classical Algorithm ◽  
Deep Learning Model

Abstract Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

scholarly journals Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins

2021 ◽  
Author(s):  
Ho-min Park ◽  
Yunseol Park ◽  
Joris Vankerschaver ◽  
Arnout Van Messem ◽  
Wesley De Neve ◽  
...  
Keyword(s):  
Protein Structure ◽  
Drug Design ◽  
Structure Prediction ◽  
Modern Medicine ◽  
Protein Therapeutics ◽  
Protein Drug ◽  
Host Proteins ◽  
Small Proteins ◽  
Small Molecule Therapeutics ◽  
Host Parasite

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches such as AlphaFold opens new opportunities to exploit the complexity of these macro-biomolecules for highly-specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these Anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of Anti-CRISPR proteins which are structurally homologous to the recently-discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.

scholarly journals Molecular insights into the improved clinical performance of PEGylated interferon therapeutics: a molecular dynamics perspective

RSC Advances ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 2315-2322 ◽  
Author(s):  
Dong Xu ◽  
Nikolai Smolin ◽  
Rance K. Shaw ◽  
Samuel R. Battey ◽  
Aoxiang Tao ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Therapeutic Efficacy ◽  
Clinical Performance ◽  
Pegylated Interferon ◽  
Computational Approach ◽  
Molecular Evidence ◽  
Protein Drug ◽  
Pegylated Protein

We discovered molecular evidence that links PEGylation to improved clinical performance, yet at the expense of decreased bioactivity. Our computational approach will facilitate PEGylated protein drug design and optimize its overall therapeutic efficacy.

Artificial Intelligence in Vaccine and Drug Design

2021 ◽  
pp. 131-146
Author(s):  
Sunil Thomas ◽  
Ann Abraham ◽  
Jeremy Baldwin ◽  
Sakshi Piplani ◽  
Nikolai Petrovsky
Keyword(s):  
Artificial Intelligence ◽  
Drug Design

scholarly journals Correction to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

2018 ◽  
Vol 20 (4) ◽  
Author(s):  
Yankang Jing ◽  
Yuemin Bian ◽  
Ziheng Hu ◽  
Lirong Wang ◽  
Xiang-Qun Xie
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design

scholarly journals Combining Generative Artificial Intelligence and On-Chip Synthesis for De Novo Drug Design

2020 ◽  
Author(s):  
Francesca Grisoni ◽  
Berend Huisman ◽  
Alexander Button ◽  
Michael Moret ◽  
Kenneth Atz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Design ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Amide Bond ◽  
Reaction Products ◽  
De Novo Drug Design ◽  
On Chip

<p>Automation of the molecular design-make-test-analyze cycle speeds up the identification of hit and lead compounds for drug discovery. Using deep learning for computational molecular design and a customized microfluidics platform for on-chip compound synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space defined by known LXRα agonists, and to suggest structural analogs of known ligands and novel molecular cores. To further the design of lead-like molecules and ensure compatibility with automated on-chip synthesis, this chemical space was confined to the set of virtual products obtainable from 17 different one-step reactions. Overall, 25 <i>de novo</i> generated compounds were successfully synthesized in flow via formation of sulfonamide, amide bond, and ester bond. First-pass <i>in vitro</i> activity screening of the crude reaction products in hybrid Gal4 reporter gene assays revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch re-synthesis, purification, and re-testing of 14 of these compounds confirmed that 12 of them were potent LXRα or LXRβ agonists. These results support the utilization of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.<b></b></p>

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