Machine Learning Classification Models to Improve the Docking‐based Screening: A Case of PI3K‐Tankyrase Inhibitors

Abstract

One of the major challenges in the current drug discovery is the improvement of the docking-based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target-specific scoring functions possessing high screening power. These scoring functions were built using the machine learning methods for the dual target inhibitors of PI3Kα and tankyrase, promising targets for colorectal cancer therapy. The Deep Neural Network models achieve the external test AUC ROC values of 0.96 and 0.93 for the random split and 0.90 and 0.84 for the time-based split of the PI3Kα and tankyrase inhibitors, respectively. In addition, the impact of the training set size and the actives/decoys ratio on the model quality was assessed. The study demonstrates that the optimized scoring functions could significantly improve the docking screening power for each individual target. This is very useful in the design of multitarget or selective drugs wherein the screening filters are applied in sequence.

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies

“…The docking poses obtained on the previous step were also scored using the previously developed target-oriented machine learning scoring approach [ 13 ]. The model employed in the present study was refined taking into account additional data available in the most recent version of the ChEMBL 26 database [ 20 ].…”

“…Based on our previous study [ 13 ], the structure of the TNKS2-ligand complex (PDB: 4N4V) was chosen for optimal performance of molecular docking-based virtual screening. Semi-rigid docking procedure was performed by means of Smina version 2019.10 software [ 23 ].…”

“…Taking this into account, we have chosen the molecular docking-based virtual screening as a primary tool to search for the TNKS inhibitors. To some extent, this choice was determined by our previous experience in the development of target-oriented scoring functions [ 13 ]. That study provided information about the optimal docking parameters able to enhance the virtual screening efficiency.…”

Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family

“…Existing molecular docking methods typically consists of conformation searching and a scoring function for complex binding affinity evaluation (Morris and Lim-Wilby, 2008). These molecular docking methods can produce the binding poses with acceptable accuracy, but they are less successful in scoring and active compound ranking, leading to high false positive rates in virtual screening campaigns (Berishvili et al, 2018). Furthermore, the performance of molecular docking for different targets may vary widely, especially with regard to the complexity of methyltransferase family targets.…”

“…It is therefore a practical compromise constructing a scoring function specific for SAM-dependent MTases. Many target-specific scoring functions have been constructed through different methods to improve the performance of existing scoring functions on certain targets to varying degree (Xing et al, 2017; Berishvili et al, 2018). Recently, our group developed a SAM-dependent methyl transferase-specific scoring function SAM-score using ε-SVR, and used this scoring function in discovery of a new class of DOT1L inhibitors (Wang et al, 2017).…”