Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms

Title:Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms

Volume: 21 Issue: 1

Author(s): Rajnish Kumar*, Anju Sharma, Mohammed Haris Siddiqui and Rajesh Kumar Tiwari

Affiliation:

• Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow 226028, Uttar Pradesh,India

Keywords: Artificial intelligence, drug, prediction, plasma protein binding, SVM, drug design.

Abstract: Aim and Objective: Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention.

Materials and Methods: In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds.

Results: The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively.

Conclusion: This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development.

Cite this article as:

Kumar Rajnish *, Sharma Anju , Siddiqui Haris Mohammed and Tiwari Kumar Rajesh, Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms, Combinatorial Chemistry & High Throughput Screening 2018; 21 (1) . https://dx.doi.org/10.2174/1386207321666171218121557