Structure‐Based Drug Discovery with Deep Learning**

Özçelik, Rıza; Tilborg, Derek van; Jiménez-Luna, José; Grisoni, Francesca

doi:10.1002/cbic.202200776

Cited by 27 publications

(8 citation statements)

References 221 publications

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“…Drug-discovery approaches are assisted profoundly by protein structure determination 57, 105, 110 . However, for many targets of interest, including transcription factors, structural solutions are not currently possible because of the intrinsically disordered regions within these proteins.…”

Section: Discussionmentioning

confidence: 99%

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Takahashi,

Chong,

Zhang

et al. 2023

Preprint

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Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap, an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFkB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

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Section: Discussionmentioning

confidence: 99%

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Takahashi,

Chong,

Zhang

et al. 2023

Preprint

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“…The EM utilized convergence tolerances of 10 − 5 kcal/mol for total energy change, 10 − 3 kcal/mol for the mean square root of the RMS gradient, and 10 − 5 Å for the maximum atomic displacement, employing the Smart Minimizer algorithm. For the conversion of large molecules from PDB to PDBQT format, AutoDock Tools 75 was used.…”

Section: Methodsmentioning

confidence: 99%

Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors

de Lima Menezes,

Sales Bezerra,

Nobre Oliveira

et al. 2024

Sci Rep

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Melatonin receptors MT1 and MT2 are G protein-coupled receptors that mediate the effects of melatonin, a hormone involved in circadian rhythms and other physiological functions. Understanding the molecular interactions between these receptors and their ligands is crucial for developing novel therapeutic agents. In this study, we used molecular docking, molecular dynamics simulations, and quantum mechanics calculation to investigate the binding modes and affinities of three ligands: melatonin (MLT), ramelteon (RMT), and 2-phenylmelatonin (2-PMT) with both receptors. Based on the results, we identified key amino acids that contributed to the receptor-ligand interactions, such as Gln181/194, Phe179/192, and Asn162/175, which are conserved in both receptors. Additionally, we described new meaningful interactions with Gly108/Gly121, Val111/Val124, and Val191/Val204. Our results provide insights into receptor-ligand recognition’s structural and energetic determinants and suggest potential strategies for designing more optimized molecules. This study enhances our understanding of receptor-ligand interactions and offers implications for future drug development.

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“…The importance of conditional generation algorithms is predicted to grow in the coming years. These techniques may enable the generation of molecules designed to meet specific requirements, potentially overcoming the limits of current scoring systems (for instance, because of non-additivity, activity cliffs ( Kwapien et al, 2022 ; Özçelik et al, 2022 ). A promising structure-based design may address de novo design for as-yet-undiscovered macromolecular targets ( Volkov et al, 2022 ), by producing molecules that match specific binding sites’ electrostatic and shape properties.…”

Section: Gai In Drug Discovery: Challenges and Opportunitiesmentioning

confidence: 99%

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Gangwal,

Ansari,

Ahmad

et al. 2024

Front. Pharmacol.

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There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

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Structure‐Based Drug Discovery with Deep Learning**

Cited by 27 publications

References 221 publications

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

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