Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning

Abstract

The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching.

Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction

“…Thus, the target compounds will exhibit the activities of similar compounds. Several successful target prediction techniques have been proposed in the literature [ 11 , 23 , 24 ]. For example, the authors in [ 25 ] implemented a method for activity prediction using the Multi-level Neighbourhoods of Atoms (MNA) structural descriptor.…”

“…In their study, the multilabel-predicted chemical activity profiling was successfully accomplished by SVM classifiers, and they suggest that the proposed approach can forecast the biological activities of unidentified chemicals or signal negative consequences of drug candidates. In [ 11 , 31 ], the Bayesian belief network classifier was applied to predict the compound’s target activities. The authors applied a novel technique to extend previous work, based on a convolutional neural network that uses the 2D fingerprint representation to predict the possibly bioactive molecules.…”

“…The proposed prediction model was experimentally evaluated using multiple datasets. This study used three datasets ( Table 5 , Table 6 and Table 7 ), which were described earlier in [ 43 , 44 ] and used in several studies for validating the ligand-based virtual screening methods [ 7 , 11 , 24 , 31 , 45 , 46 ].…”

“…The collection of atoms that constitute a molecule can be symbolically represented in many ways. It is not easy to determine the optimum approach that represents the molecular structure that is suited for all applications [ 9 , 10 , 11 ].…”

Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen

“…Some studies looked into data fusion and proposed that similarity measurements be merged by combining the screening results obtained by employing multiple similarity measures. Nasser et al fused several descriptors by selecting the best features from each descriptor and then merging them in the new descriptor [ 3 , 4 , 22 ]. Although the above methods outperform their predecessors, particularly when dealing with molecules with homogeneous active structural elements such as molecules’ classes in MDDR-DS2 dataset as will shown in Section 3.1 , the performances are not good or satisfactory when dealing with molecules with a structurally heterogeneous nature such as molecules’ classes in MDDR-DS3 dataset as will shown in Section 3.1 .…”