FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367182451> ?p ?o ?g. }

• W4367182451 endingPage "1340" @default.
• W4367182451 startingPage "1340" @default.
• W4367182451 abstract "In rational drug design, the concept of molecular similarity searching is frequently used to identify molecules with similar functionalities by looking up structurally related molecules in chemical databases. Different methods have been developed to measure the similarity of molecules to a target query. Although the approaches perform effectively, particularly when dealing with molecules with homogenous active structures, they fall short when dealing with compounds that have heterogeneous structural compounds. In recent times, deep learning methods have been exploited for improving the performance of molecule searching due to their feature extraction power and generalization capabilities. However, despite numerous research studies on deep-learning-based molecular similarity searches, relatively few secondary research was carried out in the area. This research aims to provide a systematic literature review (SLR) on deep-learning-based molecular similarity searches to enable researchers and practitioners to better understand the current trends and issues in the field. The study accesses 875 distinctive papers from the selected journals and conferences, which were published over the last thirteen years (2010–2023). After the full-text eligibility analysis and careful screening of the abstract, 65 studies were selected for our SLR. The review’s findings showed that the multilayer perceptrons (MLPs) and autoencoders (AEs) are the most frequently used deep learning models for molecular similarity searching; next are the models based on convolutional neural networks (CNNs) techniques. The ChEMBL dataset and DrugBank standard dataset are the two datasets that are most frequently used for the evaluation of deep learning methods for molecular similarity searching based on the results. In addition, the results show that the most popular methods for optimizing the performance of molecular similarity searching are new representation approaches and reweighing features techniques, and, for evaluating the efficiency of deep-learning-based molecular similarity searching, the most widely used metrics are the area under the curve (AUC) and precision measures." @default.
• W4367182451 created "2023-04-28" @default.
• W4367182451 creator A5006630819 @default.
• W4367182451 creator A5012641332 @default.
• W4367182451 creator A5015761017 @default.
• W4367182451 date "2023-04-26" @default.
• W4367182451 modified "2023-09-26" @default.
• W4367182451 title "Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review" @default.
• W4367182451 cites W1978788686 @default.
• W4367182451 cites W2021748110 @default.
• W4367182451 cites W2083385715 @default.
• W4367182451 cites W2087818143 @default.
• W4367182451 cites W2145578524 @default.
• W4367182451 cites W2157331557 @default.
• W4367182451 cites W2168130919 @default.
• W4367182451 cites W2174991771 @default.
• W4367182451 cites W2237129204 @default.
• W4367182451 cites W2290847742 @default.
• W4367182451 cites W2327127510 @default.
• W4367182451 cites W2513486311 @default.
• W4367182451 cites W2528161821 @default.
• W4367182451 cites W2540540415 @default.
• W4367182451 cites W2549738163 @default.
• W4367182451 cites W2567534979 @default.
• W4367182451 cites W2578240541 @default.
• W4367182451 cites W2592742128 @default.
• W4367182451 cites W2607497028 @default.
• W4367182451 cites W2619754162 @default.
• W4367182451 cites W2736137960 @default.
• W4367182451 cites W2757784215 @default.
• W4367182451 cites W2777416523 @default.
• W4367182451 cites W2782719864 @default.
• W4367182451 cites W2784213390 @default.
• W4367182451 cites W2789796452 @default.
• W4367182451 cites W2807792492 @default.
• W4367182451 cites W2860192827 @default.
• W4367182451 cites W2891838559 @default.
• W4367182451 cites W2891868449 @default.
• W4367182451 cites W2899788782 @default.
• W4367182451 cites W2906755148 @default.
• W4367182451 cites W2911107164 @default.
• W4367182451 cites W2914877211 @default.
• W4367182451 cites W2943890584 @default.
• W4367182451 cites W2944851425 @default.
• W4367182451 cites W2948452024 @default.
• W4367182451 cites W2949159188 @default.
• W4367182451 cites W2954996726 @default.
• W4367182451 cites W2957050889 @default.
• W4367182451 cites W2962764460 @default.
• W4367182451 cites W2963946985 @default.
• W4367182451 cites W2964880320 @default.
• W4367182451 cites W2966357564 @default.
• W4367182451 cites W2969996838 @default.
• W4367182451 cites W2970686841 @default.
• W4367182451 cites W2974015590 @default.
• W4367182451 cites W2976294194 @default.
• W4367182451 cites W2978484973 @default.
• W4367182451 cites W2986000199 @default.
• W4367182451 cites W2987553406 @default.
• W4367182451 cites W2997728144 @default.
• W4367182451 cites W2997833216 @default.
• W4367182451 cites W2998571806 @default.
• W4367182451 cites W2999309192 @default.
• W4367182451 cites W2999554466 @default.
• W4367182451 cites W3000043291 @default.
• W4367182451 cites W3004273418 @default.
• W4367182451 cites W3004604833 @default.
• W4367182451 cites W3004919484 @default.
• W4367182451 cites W3006714852 @default.
• W4367182451 cites W3010821291 @default.
• W4367182451 cites W3012107310 @default.
• W4367182451 cites W3013529009 @default.
• W4367182451 cites W3014225643 @default.
• W4367182451 cites W3014974602 @default.
• W4367182451 cites W3018980093 @default.
• W4367182451 cites W3023204497 @default.
• W4367182451 cites W3023532008 @default.
• W4367182451 cites W3025593963 @default.
• W4367182451 cites W3030948478 @default.
• W4367182451 cites W3032123378 @default.
• W4367182451 cites W3036230820 @default.
• W4367182451 cites W3037682562 @default.
• W4367182451 cites W3039465695 @default.
• W4367182451 cites W3042826782 @default.
• W4367182451 cites W3042995081 @default.
• W4367182451 cites W3043461363 @default.
• W4367182451 cites W3046893242 @default.
• W4367182451 cites W3047273263 @default.
• W4367182451 cites W3085429933 @default.
• W4367182451 cites W3087318293 @default.
• W4367182451 cites W3092193327 @default.
• W4367182451 cites W3096097684 @default.
• W4367182451 cites W3098269892 @default.
• W4367182451 cites W3098306883 @default.
• W4367182451 cites W3102745472 @default.
• W4367182451 cites W3110901318 @default.
• W4367182451 cites W3112376181 @default.
• W4367182451 cites W3113447514 @default.

• next