Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Li, H.; Yap, Chun Wei; Ung, Choong Yong; Yang, Xue; Li, Z.R.; Han, Lianyi; Lin, Honghuang; Chen, Yu Zong

doi:10.1002/jps.20985

Cited by 61 publications

(45 citation statements)

References 117 publications

(172 reference statements)

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“…In step 2, following the description by Shi, et al [8], a total of 98 molecular descriptors were calculated and SVM models were constructed in this study. VS performance in screening large chemical libraries is measured by several indicators [9], including yield (percentage of known positives predicted as virtual hits), hit-rate (percentage of virtual hits that are known positives), false-hit rate (percentage of virtual hits that are known negatives) and enrichment factor (magnitude of hit-rate improvement over random selection from chemical libraries). In this study, virtual hit and false-hit rate in searching large chemical libraries were evaluated by using 13.56M PubChem and 168K MDDR compounds [8].…”

Section: P Wang Et Al / Identification Of Dual Active Agents Targetmentioning

confidence: 99%

Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods

Wang

Yang

et al. 2015

BME

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Abstract. Selective serotonin reuptake inhibitors (SSRIs) are most adopted therapeutics marketed for major depression, and the efficacy of which are greatly reduced by their delayed onset of action and undesirable side effects. 5-HT1A receptor partial agonist and SERT inhibitor (SPARI) was proposed as a novel strategy to overcome the shortage of efficacy by a negative feedback control of 5-HT1A receptor. However, only one SPARI (vilazodone) has been approved for clinical use, and none is currently in clinical trial, which demonstrates a strong need for searching more novel SPARIs to facilitate antidepressants discovery. This work applied a combinatorial virtual screening method (CVSM) by integrating multiple tools. Statistic analysis reveals that CVSM surpasses single virtual screening methods in terms of hit rates and enrichment factors. By adopting optimized CVSM, 91 promising dual target leads form 15 scaffolds were identified, and 40% of these scaffolds have already been reported to show antidepressant related therapeutic effects. In sum, CVSM is capable in identifying novel SPARIs from large chemical libraries with extremely low false hit rate.

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Section: P Wang Et Al / Identification Of Dual Active Agents Targetmentioning

confidence: 99%

Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods

Wang

Yang

et al. 2015

BME

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“…The methods and issues, which have been thoroughly reviewed, fall into two categories: empirical, data-based approaches (Stouch et al, 2003;Beresford et al, 2004;Yamashita and Hashida, 2004;Li et al, 2007) and structure-based approaches (Ng et al, 2004;Li et al, 2007). Data-based approaches can be subdivided into linear and nonlinear methods, and each approach may involve clustering.…”

Section: Discussionmentioning

confidence: 99%

Predictions of Hepatic Disposition Properties Using a Mechanistically Realistic, Physiologically Based Model

Sheihk-Bahaei²,

Park³

et al. 2008

Drug Metabolism and Disposition

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ABSTRACT:Quantitative mappings were established between drug physicochemical properties (PCPs) and parameter values of a physiologically based, mechanistically realistic, in silico liver (ISL). The ISL plugs together autonomous software objects that represent hepatic components at different scales and levels of detail. Microarchitectural features are represented separately from the mechanisms that influence drug metabolism. The same ISL has been validated against liver perfusion data for sucrose and four cationic drugs: antipyrine, atenolol, labetalol, and diltiazem. Parameters sensitive to drug-specific PCPs were tuned so that ISL outflow profiles from a single ISL matched in situ perfused rat liver outflow profiles of all five compounds. Quantitative relationships were then established between the four sets of drug PCPs and the corresponding four sets of PCP-sensitive, ISL parameter values; those relationships were used to predict PCP-sensitive, ISL parameter values for prazosin and propranolol given only their PCPs. Relationships were established using three different methods: 1) a simple linear correlation method, 2) the fuzzy c-means algorithm, and 3) a simple artificial neural network. Each relationship was used separately to predict ISL parameter values for prazosin and propranolol, given their PCPs. Those values were applied in the ISL used earlier to predict the hepatic disposition details for each drug. Although we had only sparse data available, all predicted disposition profiles were judged reasonable (within a factor of 2 of referent profile data). The order of precision, based on a similarity measure, was 3 > 2 > 1.The in silico liver (ISL) Yan et al., 2007) (Fig. 1) is a first-generation example of synthetic, discrete, physiologically based analogs that are intended for refining, exploring, and testing hypotheses about the details of hepatic drug disposition. Autonomous components represent spatial aspects of hepatic organization and function. Different drugs can be represented and studied simultaneously or separately. Each ISL component can interact uniquely with any drug-representing object that enters its local environment. The consequences of simulated systemic and local interactions can be measured and studied simultaneously, analogous to how wet-lab experiments are conducted.In Yan et al. (2008), the simulated hepatic disposition of atenolol, antipyrine, labetalol, and diltiazem, along with coadministered sucrose, used only one parameterized ISL structure for all compounds. A subset of components interacted differently with the particular compounds. Monte Carlo ISL variants simulated compound-specific outflow profiles that matched referent profiles. The results supported two hypotheses. 1) The mappings in Fig. 2 between ISL components and corresponding liver components were sufficiently realistic for the stated model use.2) The simulated drug-ISL component interaction events mapped to corresponding hepatic disposition events. A goal for this project has been to discover and verify ISL count...

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“…ADMET processes often involve interaction with the associated proteins [13]. For example, the cytochrome P450 (CYP) isoenzymes, such as CYP 3A4, 2D6, and 2C9, are responsible proteins for the metabolism of most drug molecules.…”

Section: Classification Of Cyp 3a4 Substrates and Non-substratesmentioning

confidence: 99%

“…A grid search based on leave-one-out cross validation was used to determine the best parameter values (C=4, ν=0.95 and γ=0.03125). The procedure of backward-elimination was applied to produce the final SVR model with 13 …”

Section: Svr Modelmentioning

confidence: 99%

Atom Coloring for Chemical Interpretation and De Novo Design for Molecular Design

Hasegawa¹,

Migita²,

Funatsu³

2011

Knowledge-Oriented Applications in Data Mining

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Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Cited by 61 publications

References 117 publications

Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods

Identification of dual active agents targeting 5-HT1A and SERT by combinatorial virtual screening methods

Predictions of Hepatic Disposition Properties Using a Mechanistically Realistic, Physiologically Based Model

Atom Coloring for Chemical Interpretation and De Novo Design for Molecular Design

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