Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

Abstract

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

“…In this respect it would be interesting to investigate the use of non-alignment dependent descriptors such as PROFEAT [42], CTD [43], or descriptors from the PROPY package [44]. …”

In silico methods for drug repurposing and pharmacology

“…As an alternative to binary classification, one can also formulate DTI prediction as a regression problem where the aim is to estimate binding affinities. Examples of this include the work of Bock and Gough 87 , in which support vector regression was used to identify high-affinity ligands for orphan GPCRs; and the more recent work of Cao et al 88 in which random forest regression on both drug and target features achieved AUC's of up to 0.96.…”

Adopting Multivariate Nonparametric Tools to Determine Genotype-Phenotype Interactions in Health and Disease

“…A good example of the application with success of RF into this setup can be found in the prediction of sulfotyrosine binding sites, where the RF are winning option to SVM, ANN, and hidden Markov models (HMM) [46]. Their performance was still highly scored in the integration of chemical, genomic [47], and pharmacological information to determine drug-target interactions [48], where they can be compared to other methods such SVM [49], to help in the assessment of the reliability of the results.…”