FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376608712> ?p ?o ?g. }

• W4376608712 endingPage "19071" @default.
• W4376608712 startingPage "19057" @default.
• W4376608712 abstract "DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure-activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections." @default.
• W4376608712 created "2023-05-17" @default.
• W4376608712 creator A5007473150 @default.
• W4376608712 creator A5010636698 @default.
• W4376608712 creator A5029762654 @default.
• W4376608712 creator A5064268420 @default.
• W4376608712 creator A5074942308 @default.
• W4376608712 date "2023-05-15" @default.
• W4376608712 modified "2023-10-15" @default.
• W4376608712 title "Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries" @default.
• W4376608712 cites W1968753037 @default.
• W4376608712 cites W1981164608 @default.
• W4376608712 cites W1985377830 @default.
• W4376608712 cites W1987857872 @default.
• W4376608712 cites W1988037271 @default.
• W4376608712 cites W1994853400 @default.
• W4376608712 cites W1999798000 @default.
• W4376608712 cites W2012016911 @default.
• W4376608712 cites W2014367004 @default.
• W4376608712 cites W2017423606 @default.
• W4376608712 cites W2025667580 @default.
• W4376608712 cites W2029209483 @default.
• W4376608712 cites W2034434306 @default.
• W4376608712 cites W2039134711 @default.
• W4376608712 cites W2046219070 @default.
• W4376608712 cites W2047178318 @default.
• W4376608712 cites W2049505969 @default.
• W4376608712 cites W2054220073 @default.
• W4376608712 cites W2062514422 @default.
• W4376608712 cites W2069506489 @default.
• W4376608712 cites W2080847356 @default.
• W4376608712 cites W2085341040 @default.
• W4376608712 cites W2099616027 @default.
• W4376608712 cites W2103459634 @default.
• W4376608712 cites W2119923393 @default.
• W4376608712 cites W2143200251 @default.
• W4376608712 cites W2155475165 @default.
• W4376608712 cites W2171947653 @default.
• W4376608712 cites W2244890058 @default.
• W4376608712 cites W2286829287 @default.
• W4376608712 cites W2289600462 @default.
• W4376608712 cites W2301707637 @default.
• W4376608712 cites W2329189826 @default.
• W4376608712 cites W2337918212 @default.
• W4376608712 cites W2342256030 @default.
• W4376608712 cites W2344864259 @default.
• W4376608712 cites W2490947343 @default.
• W4376608712 cites W2559724803 @default.
• W4376608712 cites W2562376843 @default.
• W4376608712 cites W2580819390 @default.
• W4376608712 cites W2595526189 @default.
• W4376608712 cites W2725528336 @default.
• W4376608712 cites W2737498154 @default.
• W4376608712 cites W2766549992 @default.
• W4376608712 cites W2767766269 @default.
• W4376608712 cites W2767891136 @default.
• W4376608712 cites W2782995788 @default.
• W4376608712 cites W2783547332 @default.
• W4376608712 cites W2787894218 @default.
• W4376608712 cites W2788063843 @default.
• W4376608712 cites W2789688845 @default.
• W4376608712 cites W2789832072 @default.
• W4376608712 cites W2793350103 @default.
• W4376608712 cites W2797097787 @default.
• W4376608712 cites W2804249900 @default.
• W4376608712 cites W2808696043 @default.
• W4376608712 cites W2900794471 @default.
• W4376608712 cites W2911925130 @default.
• W4376608712 cites W2913049367 @default.
• W4376608712 cites W2933089742 @default.
• W4376608712 cites W2937307539 @default.
• W4376608712 cites W2949576014 @default.
• W4376608712 cites W2951336522 @default.
• W4376608712 cites W2981035632 @default.
• W4376608712 cites W2981109304 @default.
• W4376608712 cites W2982365748 @default.
• W4376608712 cites W2999977264 @default.
• W4376608712 cites W3005945993 @default.
• W4376608712 cites W3018214857 @default.
• W4376608712 cites W3020746848 @default.
• W4376608712 cites W3027461886 @default.
• W4376608712 cites W3028123276 @default.
• W4376608712 cites W3034612930 @default.
• W4376608712 cites W3035028838 @default.
• W4376608712 cites W3092211024 @default.
• W4376608712 cites W3110402266 @default.
• W4376608712 cites W3110723544 @default.
• W4376608712 cites W3113314457 @default.
• W4376608712 cites W3115530920 @default.
• W4376608712 cites W3120107192 @default.
• W4376608712 cites W3124172763 @default.
• W4376608712 cites W3127345465 @default.
• W4376608712 cites W3132517949 @default.
• W4376608712 cites W3137487673 @default.
• W4376608712 cites W3157629089 @default.
• W4376608712 cites W3158382892 @default.
• W4376608712 cites W3165393082 @default.
• W4376608712 cites W3166642179 @default.

• next