FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311326710> ?p ?o ?g. }

• W4311326710 endingPage "47546" @default.
• W4311326710 startingPage "47536" @default.
• W4311326710 abstract "Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted." @default.
• W4311326710 created "2022-12-25" @default.
• W4311326710 creator A5026257448 @default.
• W4311326710 creator A5089844490 @default.
• W4311326710 date "2022-12-13" @default.
• W4311326710 modified "2023-10-09" @default.
• W4311326710 title "Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point" @default.
• W4311326710 cites W1573637110 @default.
• W4311326710 cites W1973673666 @default.
• W4311326710 cites W1988037271 @default.
• W4311326710 cites W1994126845 @default.
• W4311326710 cites W2000189060 @default.
• W4311326710 cites W2014858249 @default.
• W4311326710 cites W2020396120 @default.
• W4311326710 cites W2031449719 @default.
• W4311326710 cites W2038561883 @default.
• W4311326710 cites W2041473955 @default.
• W4311326710 cites W2051669906 @default.
• W4311326710 cites W2052017927 @default.
• W4311326710 cites W2054203041 @default.
• W4311326710 cites W2062533676 @default.
• W4311326710 cites W2067361851 @default.
• W4311326710 cites W2067643341 @default.
• W4311326710 cites W2068270570 @default.
• W4311326710 cites W2068542212 @default.
• W4311326710 cites W2072332617 @default.
• W4311326710 cites W2087561463 @default.
• W4311326710 cites W2096560421 @default.
• W4311326710 cites W2107081909 @default.
• W4311326710 cites W2126138337 @default.
• W4311326710 cites W2139932193 @default.
• W4311326710 cites W2147479689 @default.
• W4311326710 cites W2159887157 @default.
• W4311326710 cites W2189911347 @default.
• W4311326710 cites W2200017991 @default.
• W4311326710 cites W2234529989 @default.
• W4311326710 cites W226418965 @default.
• W4311326710 cites W2269909407 @default.
• W4311326710 cites W2276859037 @default.
• W4311326710 cites W2325195519 @default.
• W4311326710 cites W2343227460 @default.
• W4311326710 cites W2409723900 @default.
• W4311326710 cites W2414542934 @default.
• W4311326710 cites W2467309505 @default.
• W4311326710 cites W2481808473 @default.
• W4311326710 cites W2538276222 @default.
• W4311326710 cites W2585717258 @default.
• W4311326710 cites W2588652753 @default.
• W4311326710 cites W2591077138 @default.
• W4311326710 cites W2607224862 @default.
• W4311326710 cites W2610646689 @default.
• W4311326710 cites W2612060048 @default.
• W4311326710 cites W2618018983 @default.
• W4311326710 cites W2773405722 @default.
• W4311326710 cites W2777416523 @default.
• W4311326710 cites W2784024079 @default.
• W4311326710 cites W2786477584 @default.
• W4311326710 cites W2788824933 @default.
• W4311326710 cites W2790016800 @default.
• W4311326710 cites W2801088198 @default.
• W4311326710 cites W2803318906 @default.
• W4311326710 cites W2809634777 @default.
• W4311326710 cites W2810408942 @default.
• W4311326710 cites W2887039457 @default.
• W4311326710 cites W2887381903 @default.
• W4311326710 cites W2891503716 @default.
• W4311326710 cites W2892653600 @default.
• W4311326710 cites W2898098684 @default.
• W4311326710 cites W2900090807 @default.
• W4311326710 cites W2911107164 @default.
• W4311326710 cites W2911612351 @default.
• W4311326710 cites W2914969288 @default.
• W4311326710 cites W2918544128 @default.
• W4311326710 cites W2922283117 @default.
• W4311326710 cites W2944466104 @default.
• W4311326710 cites W2951676304 @default.
• W4311326710 cites W2952522777 @default.
• W4311326710 cites W2963374347 @default.
• W4311326710 cites W2964303497 @default.
• W4311326710 cites W3014780529 @default.
• W4311326710 cites W3021370268 @default.
• W4311326710 cites W3021575798 @default.
• W4311326710 cites W3027099090 @default.
• W4311326710 cites W3102363003 @default.
• W4311326710 cites W3112550610 @default.
• W4311326710 cites W3116202926 @default.
• W4311326710 cites W3116490021 @default.
• W4311326710 cites W3133543405 @default.
• W4311326710 cites W3136947284 @default.
• W4311326710 cites W3165163721 @default.
• W4311326710 cites W3186302820 @default.
• W4311326710 cites W3196868860 @default.
• W4311326710 cites W3208548154 @default.
• W4311326710 cites W3196377914 @default.
• W4311326710 doi "https://doi.org/10.1021/acsomega.2c05693" @default.
• W4311326710 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36591139" @default.
• W4311326710 hasPublicationYear "2022" @default.
• W4311326710 type Work @default.

• next