scholarly journals Chemical Structure Indices in In Silico Molecular Design

2008 ◽  
Vol 76 (2) ◽  
pp. 101-132 ◽  
Author(s):  
Yenamandra Prabhakar
Keyword(s):  
In Silico ◽  
Chemical Structure ◽  
Molecular Design ◽  
Structure Indices

scholarly journals Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Reiko Watanabe ◽  
Rikiya Ohashi ◽  
Tsuyoshi Esaki ◽  
Hitoshi Kawashima ◽  
Yayoi Natsume-Kitatani ◽  
...  
Keyword(s):  
Renal Clearance ◽  
In Silico ◽  
Regression Models ◽  
Chemical Structure ◽  
Prediction Models ◽  
Renal Excretion ◽  
Early Stage ◽  
Classification Model ◽  
Prediction System ◽  
Structure Information

AbstractPrediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

scholarly journals In-silico molecular design of heterocyclic benzimidazole scaffolds as prospective anticancer agents

BMC Chemistry ◽  
2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Sumit Tahlan ◽  
Sanjiv Kumar ◽  
Kalavathy Ramasamy ◽  
Siong Meng Lim ◽  
Syed Adnan Ali Shah ◽  
...  
Keyword(s):  
Anticancer Agents ◽  
In Silico ◽  
Molecular Design

Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds

2003 ◽  
Vol 9 (6) ◽  
pp. 395-407 ◽  
Author(s):  
Humberto Gonzáles-Díaz ◽  
Ornella Gia ◽  
Eugenio Uriarte ◽  
Ivan Hernádez ◽  
Ronal Ramos ◽  
...  
Keyword(s):  
In Silico ◽  
Molecular Design ◽  
Anticancer Compounds ◽  
In Silico Design ◽  
Computer Aided

scholarly journals Impact of Chemical Structure on Conjunctival Drug Permeability: Adopting Porcine Conjunctiva and Cassette Dosing for Construction of In Silico Model

2017 ◽  
Vol 106 (9) ◽  
pp. 2463-2471 ◽  
Author(s):  
Eva Ramsay ◽  
Marika Ruponen ◽  
Théo Picardat ◽  
Unni Tengvall ◽  
Marjo Tuomainen ◽  
...  
Keyword(s):  
In Silico ◽  
Chemical Structure ◽  
Drug Permeability ◽  
In Silico Model ◽  
Cassette Dosing

Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited

1992 ◽  
Vol 32 (3) ◽  
pp. 244-255 ◽  
Author(s):  
Arthur Dalby ◽  
James G. Nourse ◽  
W. Douglas Hounshell ◽  
Ann K. I. Gushurst ◽  
David L. Grier ◽  
...  
Keyword(s):  
Chemical Structure ◽  
Molecular Design ◽  
Computer Programs ◽  
File Formats ◽  
Structure File

scholarly journals Explaining and avoiding failures modes in goal-directed generation

2021 ◽  
Author(s):  
Maxime Langevin ◽  
Rodolphe Vuilleumier ◽  
Marc Bianciotto
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Optimization Model ◽  
In Silico ◽  
Molecular Design ◽  
Data Distribution ◽  
Learning Models ◽  
Control Models ◽  
Machine Learning Models

Despite growing interest and success in automated in-silico molecular design, doubts remain regarding the ability of goal-directed generation algorithms to perform unbiased exploration of novel chemical spaces. A specific phenomenon has recently been highlighted: goal-directed generation guided with machine learning models produce molecules with high scores according to the optimization model, but low scores according to control models, even when trained on the same data distribution and the same target. In this work, we show that this worrisome behavior is actually due to issues with the predictive models and not the goal-directed generation algorithms. We show that with appropriate predictive models, this issue can be resolved, and molecules generated have high scores according to both the optimization and the control models.

scholarly journals Artificial Intelligence Guided De Novo Molecular Design Targeting COVID-19

2020 ◽  
Author(s):  
Srilok Srinivasan ◽  
Rohit Batra ◽  
Henry Chan ◽  
Ganesh Kamath ◽  
Mathew J. Cherukara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
In Silico ◽  
High Throughput Screening ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Computational Cost ◽  
Ligand Design ◽  
Therapeutic Agents ◽  
Docking Simulations

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.

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