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  • 1
    Online Resource
    Online Resource
    Cambridge University Press (CUP) ; 2022
    In:  QRB Discovery Vol. 3 ( 2022)
    In: QRB Discovery, Cambridge University Press (CUP), Vol. 3 ( 2022)
    Abstract: RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.
    Type of Medium: Online Resource
    ISSN: 2633-2892
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2022
    detail.hit.zdb_id: 3049859-4
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  • 2
    Online Resource
    Online Resource
    Frontiers Media SA ; 2022
    In:  Frontiers in Pharmacology Vol. 13 ( 2022-6-29)
    In: Frontiers in Pharmacology, Frontiers Media SA, Vol. 13 ( 2022-6-29)
    Abstract: The choice of target pocket is a key step in a drug discovery campaign. This step can be supported by in silico druggability prediction. In the literature, druggability prediction is often approached as a two-class classification task that distinguishes between druggable and non-druggable (or less druggable) pockets (or voxels). Apart from obvious cases, however, the non-druggable class is conceptually ambiguous. This is because any pocket (or target) is only non-druggable until a drug is found for it. It is therefore more appropriate to adopt a one-class approach, which uses only unambiguous information, namely, druggable pockets. Here, we propose using the import vector domain description (IVDD) algorithm to support this task. IVDD is a one-class probabilistic kernel machine that we previously introduced. To feed the algorithm, we use customized DrugPred descriptors computed via NanoShaper. Our results demonstrate the feasibility and effectiveness of the approach. In particular, we can remove or mitigate biases chiefly due to the labeling.
    Type of Medium: Online Resource
    ISSN: 1663-9812
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2587355-6
    SSG: 15,3
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  • 3
    Online Resource
    Online Resource
    AIP Publishing ; 2023
    In:  The Journal of Chemical Physics Vol. 158, No. 16 ( 2023-04-28)
    In: The Journal of Chemical Physics, AIP Publishing, Vol. 158, No. 16 ( 2023-04-28)
    Abstract: Allostery is a constitutive, albeit often elusive, feature of biomolecular systems, which heavily determines their functioning. Its mechanical, entropic, long-range, ligand, and environment-dependent nature creates far from trivial interplays between residues and, in general, the secondary structure of proteins. This intricate scenario is mirrored in computational terms as different notions of “correlation” among residues and pockets can lead to different conclusions and outcomes. In this article, we put on a common ground and challenge three computational approaches for the correlation estimation task and apply them to three diverse targets of pharmaceutical interest: the androgen A2A receptor, the androgen receptor, and the EGFR kinase domain. Results show that partial results consensus can be attained, yet different notions lead to pointing the attention to different pockets and communications.
    Type of Medium: Online Resource
    ISSN: 0021-9606 , 1089-7690
    Language: English
    Publisher: AIP Publishing
    Publication Date: 2023
    detail.hit.zdb_id: 3113-6
    detail.hit.zdb_id: 1473050-9
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