scholarly journals Chemical Space Expansion of Bromodomain Ligands Guided by in Silico Virtual Couplings (AutoCouple)

2018 ◽  
Vol 4 (2) ◽  
pp. 180-188 ◽  
Author(s):  
Laurent Batiste ◽  
Andrea Unzue ◽  
Aymeric Dolbois ◽  
Fabrice Hassler ◽  
Xuan Wang ◽  
...  
2008 ◽  
Vol 14 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Andrew W. Knight ◽  
Louise Birrell ◽  
Richard M. Walmsley

There is a pressing need to develop rapid yet accurate screening assays for the identification of genotoxic liability and for early hazard assessment in drug discovery. The GADD45a-GFP human cell-based genotoxicity assay (GreenScreen HC) has been reformatted to test 12 compounds per 96-well microplate in a higher throughput, automated screening mode and the protocol applied to the analysis of 1266 diverse, pharmacologically active compounds. Testing from a fixed starting concentration of 100 µM and over 3 serial dilutions, the hit rates for genotoxicity (7.3%) and cytotoxicity (33%) endpoints of the assay have been determined in a much wider chemical space than previously reported. The degree of interference from color, autofluorescence, and low solubility has also been assessed. The assay results have been compared to an in silico approach to genotoxicity assessment using Derek for Windows software. Where carcinogenicity data were available, GreenScreen HC demonstrated a higher specificity than in silico methods while identifying genotoxic species that were not highlighted for genotoxic liability in structure-activity relationship software. Higher throughput screening from a fixed, low concentration reduces sensitivity to less potent genotoxins, but the maintenance of the previously reported high specificity is essential in early hazard assessment where misclassification can lead to the needless rejection of potentially useful compounds in drug development. ( Journal of Biomolecular Screening 2009:16-30)


2017 ◽  
Vol 34 (9) ◽  
pp. 1061-1089 ◽  
Author(s):  
Xingwang Zhang ◽  
Shengying Li

This review focuses on unusual P450 reactions related to new chemistry, skeleton construction, structure re-shaping, and protein–protein interactions in natural product biosynthesis, which play significant roles in chemical space expansion for natural products.


2019 ◽  
Author(s):  
Robin Winter ◽  
Floriane Montanari ◽  
Andreas Steffen ◽  
Hans Briem ◽  
Frank Noé ◽  
...  

In this work, we propose a novel method that combines in silico prediction of molecular properties such as biological activity or pharmacokinetics with an in silico optimization algorithm, namely Particle Swarm Optimization. Our method takes a starting compound as input and proposes new molecules with more desirable (predicted) properties. It navigates a machine-learned continuous representation of a drug-like chemical space guided by a de fined objective function. The objective function combines multiple in silico prediction models, de fined desirability ranges and substructure constraints. We demonstrate that our proposed method is able to consistently fi nd more desirable molecules for the studied tasks in relatively short time.<br>


2020 ◽  
Author(s):  
Srilok Srinivasan ◽  
Rohit Batra ◽  
Henry Chan ◽  
Ganesh Kamath ◽  
Mathew J. Cherukara ◽  
...  

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. Computational docking simulations have traditionally been used for <i>in silico</i> ligand design and remain popular method of choice for high-throughput screening of therapeutic agents in the fight against COVID-19. Despite the vast chemical space (millions to billions of biomolecules) that can be potentially explored as therapeutic agents, we remain severely limited in the search of candidate compounds owing to the high computational cost of these ensemble docking simulations employed in traditional <i>in silico</i> ligand design. Here, we present a <i>de novo</i> molecular design strategy that leverages artificial intelligence to discover new therapeutic biomolecules against SARS-CoV-2. A Monte Carlo Tree Search algorithm combined with a multi-task neural network (MTNN) surrogate model for expensive docking simulations and recurrent neural networks (RNN) for rollouts, is used to sample the exhaustive SMILES space of candidate biomolecules. Using Vina scores as target objective to measure binding of therapeutic molecules to either the isolated spike protein (S-protein) of SARS-CoV-2 at its host receptor region or to the S-protein:Angiotensin converting enzyme 2 (ACE2) receptor interface, we generate several (~100's) new biomolecules that outperform FDA (~1000’s) and non-FDA biomolecules (~million) from existing databases. A transfer learning strategy is deployed to retrain the MTNN surrogate as new candidate molecules are identified - this iterative search and retrain strategy is shown to accelerate the discovery of desired candidates. We perform detailed analysis using Lipinski's rules and also analyze the structural similarities between the various top performing candidates. We spilt the molecules using a molecular fragmenting algorithm and identify the common chemical fragments and patterns – such information is important to identify moieties that are responsible for improved performance. Although we focus on therapeutic biomolecules, our AI strategy is broadly applicable for accelerated design and discovery of any chemical molecules with user-desired functionality.


2019 ◽  
Vol 15 (1) ◽  
pp. 103-108 ◽  
Author(s):  
Nanjan Pandurangan ◽  
Chinchu Bose ◽  
Sreejith Meppoyilam ◽  
Veni C. Kalathil ◽  
Anjana Murali ◽  
...  

Background: During last two decades, good progress has been made for the flavonoids in metabolic and infectious diseases. However, expectations have not been fulfilled when these compounds were extended to the in vivo studies, particularly in humans. This could due to low bioavailability and less absorption of flavonoids in the biological systems. A recent study revealed that methylation of flavonoids can bring about dramatic changes in pharmacokinetic and biochemical properties. Often, the partially methylated flavonoids show better activities by improving their metabolic stability, membrane transport capacity, facilitating absorption and for oral bio-availability. Though, partial methyl ethers occupy a large chemical space, their biological properties has not been well established. Azaleatin (quercetin-5-O-methyl ether) is one of such group of naturally occurring molecules. Methods: In the present study, we have utilized methoxymethyl (MOM) protecting groups for the preparation of azaleatin. Synthesized compounds and their derivatives were subjected for &amp;#945;-Amylase and DPPH activities. &amp;#945;-Amylase activity can be measured in vitro by hydrolysis of starch in presence of &amp;#945;-amylase enzyme. Antioxidant capacity was evaluated by measuring the scavenging activity of azaleatin and related compounds on the 2,2- diphenyl-l-1-picrylhydrazil (DPPH) radical. In order to identify the binding mode of the compound azaleatin, Auto Dock Tools (http://mgltools.scripps.edu) were used. Results: Quercetin, along with their derivatives, monomethyl ethers i.e. azaleatin, isorhamnetin, tamarixetin; dimethyl ether i.e. quercetin-3,7-dimethyl ether; quercetin-3,3&amp;#039;,7-trimethyletherpachypodol; quercetin-3,3&#039;,4&#039;7-tetramethyl ether and quercetin pentamethyl ether were taken for &amp;#945;- amylase inhibitory activity. The study showed that azaleatin was found to be comparable with the standard for the inhibition of &amp;#945;-amylase amongst the tested compounds. Since, azaleatin is a best for the inhibition of &amp;#945;-amylase, this compound was taken for the in-silico molecular modelling studies. Azaleatin, showed a good docking energy (-8.8 Kcalmol-1) with the &amp;#945;-amylase receptor. Similarly, other closely related derivatives were studied for their antioxidant capacity. It was found that amongst the compound tested quercetin was found to be best (EC50 of 30&amp;#181;g/mL) for their antioxidant capacity and second best compound was azaleatin; which showed EC50 of 36.1&amp;#181;g/mL. Conclusion: An efficient synthesis of azaleatin, a lesser known flavone has been achieved from quercetin. Amongst the compounds tested, azaleatin was found to inhibit &amp;#945;-amylase with the acceptable radical scavenging activity. Further, in-silico modelling studies indicated that azaleatin found to have very good affinity with the key residues i.e. Gln63, Asp197 and Arg195 of &amp;#945;-amylase receptor. Since, azaleatin has other free hydroxyls in their template, it can be effectively utilized for the activity improvement through further structural modifications.


2012 ◽  
Vol 12 (3) ◽  
pp. 449-457 ◽  
Author(s):  
Patrik Muigg ◽  
Josefin Rosén ◽  
Lars Bohlin ◽  
Anders Backlund

2017 ◽  
Vol 90 (2) ◽  
pp. 175-187 ◽  
Author(s):  
Paola Santos ◽  
Fabian López-Vallejo ◽  
Carlos-Y. Soto

2020 ◽  
Vol 36 (13) ◽  
pp. 4093-4094
Author(s):  
Robin Winter ◽  
Joren Retel ◽  
Frank Noé ◽  
Djork-Arné Clevert ◽  
Andreas Steffen

Abstract Summary Optimizing small molecules in a drug discovery project is a notoriously difficult task as multiple molecular properties have to be considered and balanced at the same time. In this work, we present our novel interactive in silico compound optimization platform termed grünifai to support the ideation of the next generation of compounds under the constraints of a multiparameter objective. grünifai integrates adjustable in silico models, a continuous representation of the chemical space, a scalable particle swarm optimization algorithm and the possibility to actively steer the compound optimization through providing feedback on generated intermediate structures. Availability and implementation Source code and documentation are freely available under an MIT license and are openly available on GitHub (https://github.com/jrwnter/gruenifai). The backend, including the optimization method and distribution on multiple GPU nodes is written in Python 3. The frontend is written in ReactJS.


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