The current impact of water thermodynamics for small-molecule drug discovery

Geschwindner, Stefan; Ulander, Johan

doi:10.1080/17460441.2019.1664468

Cited by 27 publications

(25 citation statements)

References 25 publications

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“…with the protein, is highly unusual. 29,30 We thus set out to explore the role of this water molecule in more detail.…”

Section: Resultsmentioning

confidence: 99%

“…Water displacement or replacement strategies have been used for ligand optimization before [36][37][38][39][40] and are especially effective for buried water molecules. However, the role of exposed solvent shell wateralthough clearly important for bindingis still poorly understood, 29,30,41 and the manipulation of water molecules that predominantly face the solvent have been rarely used for ligand optimization. 40 We propose that the observed water position (although energetically unfavorable) is still strongly populated because other water arrangements are even worse.…”

Section: Discussionmentioning

confidence: 99%

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Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides

et al. 2021

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“…with the protein, is highly unusual. 29,30 We thus set out to explore the role of this water molecule in more detail.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides

et al. 2021

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“…The binding site of a protein is flexible and can adopt diverse conformational states, generally at the level of side chain residues but often also involving structural changes in loops and the remodeling of secondary structural elements induced upon ligand binding [ 42 , 43 , 44 , 45 ]. Furthermore, the outcome of docking studies may be largely affected by the identification of water molecules that mediate the interactions of the ligand in the binding pocket, making it necessary to explore the potential role of bridging waters or networks of ordered waters in docking calculations [ 46 , 47 , 48 , 49 , 50 ]. On the other hand, providing an accurate score and even estimating the binding affinity at a reasonable cost compatible with the screening of large chemical libraries is still challenging for docking methods [ 51 , 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Vázquez

Lopez

Gibert³

et al. 2020

Molecules

135

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Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

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“…Still, our model has a room for improvement regarding the representation of solvation energy. In reality, proteins and ligands interact while surrounded by numerous water molecules, and the water molecules engender thermodynamic and structural effects on ligand binding 66…”

Section: Discussionmentioning

confidence: 99%

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

Moon¹,

Zhung²,

Yang³

et al. 2020

Preprint

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Recently, deep neural network (DNN)-based drug-target interaction (DTI) models are highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in-silico drug discovery.We propose two key strategies to enhance generalization in the DTI model. The first one is to integrate physical models into DNN models. Our model, PIGNet, predicts the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a wider range of binding poses and ligands to training data. PIGNet achieved a significant improvement in docking success rate, screening enhancement factor, and screening success rate by up to 2.01, 10.78, 14.0 times, respectively, compared to the previous DNN models. The physics-informed model also en-1

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The current impact of water thermodynamics for small-molecule drug discovery

Cited by 27 publications

References 25 publications

Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides

Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

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