Abstract
Traditional drug development is a slow and costly process that leads to the production of new drugs. Virtual screening (VS) is a computational procedure that measures the similarity of molecules as one of its primary tasks. Many techniques for capturing the biological similarity between a test compound and a known target ligand have been established in ligand-based virtual screens (LBVSs). However, despite the good performances of the above methods compared to their predecessors, especially when dealing with molecules that have structurally homogenous active elements, they are not satisfied when dealing with molecules that are structurally heterogeneous. The main aim of this study is to improve the performance of similarity searching, especially with molecules that are structurally heterogeneous. The Siamese network will be used due to its capability to deal with complicated data samples in many fields. The Siamese multi-layer perceptron architecture will be enhanced by using two similarity distance layers with one fused layer, then multiple layers will be added after the fusion layer, and then the nodes of the model that contribute less or nothing during inference according to their signal-to-noise ratio values will be pruned. Several benchmark datasets will be used, which are: the MDL Drug Data Report (MDDR-DS1, MDDR-DS2, and MDDR-DS3), the Maximum Unbiased Validation (MUV), and the Directory of Useful Decoys (DUD). The results show the outperformance of the proposed method on standard Tanimoto coefficient (TAN) and other methods. Additionally, it is possible to reduce the number of nodes in the Siamese multilayer perceptron model while still keeping the effectiveness of recall on the same level.
Highlights
Drug discovery is a prolonged and complex process that culminates in the manufacture of new drugs
Despite the good performances of the above methods compared to their prior, especially when dealing with molecules that have homogenous active elements structural such as classes of molecules in the MDL Drug Data Report dataset (MDDR_DR2), the performances are not satisfied when dealing with molecules with structurally heterogeneous nature such as classes of molecules in the MDL Drug Data Report dataset (MDDR_DR3, MDDR_DR1) and maximum unbiased validation (MUV) dataset
These tables show the results of the enhanced Siamese multilayer perceptron (MLP) similarity model compared to the benchmark Tanimoto coefficient (TAN), as well as earlier studies BIN, SQB, and SDBN for MDL drug drug data data report report (MDDR) datasets, BIN and SQB for Maximum Unbiased Validation (MUV) datasets, and SQB for directory of useful decoys (DUD) datasets
Summary
Drug discovery is a prolonged and complex process that culminates in the manufacture of new drugs. The biomolecular target is selected, and high-performance screening procedures are executed to identify bioactive chemicals for defined aims in traditional drug research and development. It is costly and time-consuming to produce high-performing research testing [1]. The virtual screening methodology is a computerized method for scanning large libraries of small compounds for the most likely structures with the goal of developing medication [3,4,5]. Virtual screening (VS) is used in the early stages of drug development to identify the most promising lead compounds from large chemical libraries. The development of medications has been sped up in recent years thanks to virtual screening (VS). Virtual screening is divided into two types: structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS) [6]
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