scholarly journals In silico prediction models of the induction of drug-metabolizing enzymes for drug discovery

2019 ◽  
Vol 153 (4) ◽  
pp. 186-191
Author(s):  
Kouichi Yoshinari ◽  
Mika Nagai
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Prediction Models ◽  
Drug Metabolizing Enzymes ◽  
In Silico Prediction ◽  
Metabolizing Enzymes

ADME properties evaluation in drug discovery: in silico prediction of blood–brain partitioning

2018 ◽  
Vol 22 (4) ◽  
pp. 979-990 ◽  
Author(s):  
Lu Zhu ◽  
Junnan Zhao ◽  
Yanmin Zhang ◽  
Weineng Zhou ◽  
Linfeng Yin ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
In Silico Prediction ◽  
Blood Brain ◽  
Adme Properties

scholarly journals Efficient Multi-Objective Molecular Optimization in a Continuous Latent Space

2019 ◽  
Author(s):  
Robin Winter ◽  
Floriane Montanari ◽  
Andreas Steffen ◽  
Hans Briem ◽  
Frank Noé ◽  
...  
Keyword(s):  
Objective Function ◽  
In Silico ◽  
Prediction Models ◽  
Chemical Space ◽  
In Silico Prediction ◽  
Swarm Optimization ◽  
Starting Compound ◽  
Latent Space ◽  
Novel Method ◽  
Short Time

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>

scholarly journals Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein

2019 ◽  
Vol 16 (5) ◽  
pp. 1851-1863 ◽  
Author(s):  
Rikiya Ohashi ◽  
Reiko Watanabe ◽  
Tsuyoshi Esaki ◽  
Tomomi Taniguchi ◽  
Nao Torimoto-Katori ◽  
...  
Keyword(s):  
In Silico ◽  
Prediction Models ◽  
In Silico Prediction ◽  
P Glycoprotein ◽  
Transport Potential

Pharmacogenomics: Translating Functional Genomics into Rational Therapeutics

Science ◽  
1999 ◽  
Vol 286 (5439) ◽  
pp. 487-491 ◽  
Author(s):  
William E. Evans ◽  
Mary V. Relling
Keyword(s):  
Drug Therapy ◽  
Drug Discovery ◽  
Drug Targets ◽  
Drug Disposition ◽  
Scientific Basis ◽  
Genetic Constitution ◽  
Drug Metabolizing Enzymes ◽  
Metabolizing Enzymes ◽  
Interindividual Differences ◽  
Pharmacogenomic Studies

Genetic polymorphisms in drug-metabolizing enzymes, transporters, receptors, and other drug targets have been linked to interindividual differences in the efficacy and toxicity of many medications. Pharmacogenomic studies are rapidly elucidating the inherited nature of these differences in drug disposition and effects, thereby enhancing drug discovery and providing a stronger scientific basis for optimizing drug therapy on the basis of each patient's genetic constitution.

In silico prediction of linear free energy relationship descriptors of neutral and ionic compounds

RSC Advances ◽  
2015 ◽  
Vol 5 (98) ◽  
pp. 80634-80642 ◽  
Author(s):  
Chul-Woong Cho ◽  
Stefan Stolte ◽  
Yeoung-Sang Yun ◽  
Ingo Krossing ◽  
Jorg Thöming
Keyword(s):  
Free Energy ◽  
In Silico ◽  
Prediction Models ◽  
Molar Refraction ◽  
Linear Free Energy Relationship ◽  
In Silico Prediction ◽  
Linear Free Energy ◽  
Neutral Compounds ◽  
Ionic Compounds

Prediction models for LFER descriptors – excess molar refraction (E), dipolarity/polarizability (S), H-bonding acidity (A) & basicity (B), McGowan volume (V), and interaction of cations (J+) and anions (J−) – of both ionic and neutral compounds.

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