FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220798957> ?p ?o ?g. }

• W4220798957 abstract "Development of new products often relies on the discovery of novel molecules. While conventional molecular design involves using human expertise to propose, synthesize, and test new molecules, this process can be cost and time intensive, limiting the number of molecules that can be reasonably tested. Generative modeling provides an alternative approach to molecular discovery by reformulating molecular design as an inverse design problem. Here, we review the recent advances in the state-of-the-art of generative molecular design and discusses the considerations for integrating these models into real molecular discovery campaigns. We first review the model design choices required to develop and train a generative model including common 1D, 2D, and 3D representations of molecules and typical generative modeling neural network architectures. We then describe different problem statements for molecular discovery applications and explore the benchmarks used to evaluate models based on those problem statements. Finally, we discuss the important factors that play a role in integrating generative models into experimental workflows. Our aim is that this review will equip the reader with the information and context necessary to utilize generative modeling within their domain. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning" @default.
• W4220798957 created "2022-04-03" @default.
• W4220798957 creator A5010124873 @default.
• W4220798957 creator A5030696048 @default.
• W4220798957 creator A5030957753 @default.
• W4220798957 creator A5048915657 @default.
• W4220798957 creator A5071010920 @default.
• W4220798957 date "2022-03-05" @default.
• W4220798957 modified "2023-10-17" @default.
• W4220798957 title "Generative models for molecular discovery: Recent advances and challenges" @default.
• W4220798957 cites W1974229648 @default.
• W4220798957 cites W1975147762 @default.
• W4220798957 cites W2022476850 @default.
• W4220798957 cites W2023818227 @default.
• W4220798957 cites W2034475831 @default.
• W4220798957 cites W2034549041 @default.
• W4220798957 cites W2060531713 @default.
• W4220798957 cites W2064675550 @default.
• W4220798957 cites W2078374001 @default.
• W4220798957 cites W2080635178 @default.
• W4220798957 cites W2151971404 @default.
• W4220798957 cites W2160592148 @default.
• W4220798957 cites W2578240541 @default.
• W4220798957 cites W2604320291 @default.
• W4220798957 cites W2610148085 @default.
• W4220798957 cites W2783658781 @default.
• W4220798957 cites W2798613236 @default.
• W4220798957 cites W2802872171 @default.
• W4220798957 cites W2883583109 @default.
• W4220798957 cites W2887447356 @default.
• W4220798957 cites W2891868449 @default.
• W4220798957 cites W2896506587 @default.
• W4220798957 cites W2900090807 @default.
• W4220798957 cites W2923101335 @default.
• W4220798957 cites W2945551948 @default.
• W4220798957 cites W2953128081 @default.
• W4220798957 cites W2953641781 @default.
• W4220798957 cites W2955727640 @default.
• W4220798957 cites W2965344674 @default.
• W4220798957 cites W2971690404 @default.
• W4220798957 cites W2985931096 @default.
• W4220798957 cites W2990537780 @default.
• W4220798957 cites W2991736596 @default.
• W4220798957 cites W2992072991 @default.
• W4220798957 cites W2992613109 @default.
• W4220798957 cites W3000404031 @default.
• W4220798957 cites W3003906593 @default.
• W4220798957 cites W3011847211 @default.
• W4220798957 cites W3014339631 @default.
• W4220798957 cites W3021941015 @default.
• W4220798957 cites W3023600056 @default.
• W4220798957 cites W3025593963 @default.
• W4220798957 cites W3028032948 @default.
• W4220798957 cites W3030948478 @default.
• W4220798957 cites W3042210295 @default.
• W4220798957 cites W3043461363 @default.
• W4220798957 cites W3044640842 @default.
• W4220798957 cites W3044724994 @default.
• W4220798957 cites W3045928028 @default.
• W4220798957 cites W3080742388 @default.
• W4220798957 cites W3087716567 @default.
• W4220798957 cites W3092737886 @default.
• W4220798957 cites W3094553402 @default.
• W4220798957 cites W3098269892 @default.
• W4220798957 cites W3099414221 @default.
• W4220798957 cites W3100751385 @default.
• W4220798957 cites W3101380508 @default.
• W4220798957 cites W3104956673 @default.
• W4220798957 cites W3107587236 @default.
• W4220798957 cites W3108004614 @default.
• W4220798957 cites W3112858573 @default.
• W4220798957 cites W3113447514 @default.
• W4220798957 cites W3116865743 @default.
• W4220798957 cites W3123775255 @default.
• W4220798957 cites W3134918678 @default.
• W4220798957 cites W3158331815 @default.
• W4220798957 cites W3161725201 @default.
• W4220798957 cites W3163993681 @default.
• W4220798957 cites W3167734706 @default.
• W4220798957 cites W3174976929 @default.
• W4220798957 cites W3212417448 @default.
• W4220798957 doi "https://doi.org/10.1002/wcms.1608" @default.
• W4220798957 hasPublicationYear "2022" @default.
• W4220798957 type Work @default.
• W4220798957 citedByCount "41" @default.
• W4220798957 countsByYear W42207989572022 @default.
• W4220798957 countsByYear W42207989572023 @default.
• W4220798957 crossrefType "journal-article" @default.
• W4220798957 hasAuthorship W4220798957A5010124873 @default.
• W4220798957 hasAuthorship W4220798957A5030696048 @default.
• W4220798957 hasAuthorship W4220798957A5030957753 @default.
• W4220798957 hasAuthorship W4220798957A5048915657 @default.
• W4220798957 hasAuthorship W4220798957A5071010920 @default.
• W4220798957 hasBestOaLocation W42207989572 @default.
• W4220798957 hasConcept C119857082 @default.
• W4220798957 hasConcept C127413603 @default.
• W4220798957 hasConcept C134306372 @default.
• W4220798957 hasConcept C151730666 @default.
• W4220798957 hasConcept C154945302 @default.
• W4220798957 hasConcept C167966045 @default.

• next