Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions

Segall, Matthew D.; Yusof, Iskander; Champness, Edmund J.

doi:10.1021/acs.jmedchem.5b01921

Cited by 19 publications

(14 citation statements)

References 17 publications

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“…These findings were used to synthesize a number of derivatives that displayed the expected activity. More importantly, the disclosure of a new druggable subpocket paves the way to further extend the chemical space of our class of inhibitors, addressing at the same time the so-called multiparametric optimization process …”

Section: Introductionmentioning

confidence: 99%

Integration of Enhanced Sampling Methods with Saturation Transfer Difference Experiments to Identify Protein Druggable Pockets

Magalhães¹,

Annunziato²,

Franko³

et al. 2018

J. Chem. Inf. Model.

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Saturation transfer difference (STD) is an NMR technique conventionally applied in drug discovery to identify ligand moieties relevant for binding to protein cavities. This is important to direct medicinal chemistry efforts in small-molecule optimization processes. However, STD does not provide any structural details about the ligand-target complex under investigation. Herein, we report the application of a new integrated approach, which combines enhanced sampling methods with STD experiments, for the characterization of ligand-target complexes that are instrumental for drug design purposes. As an example, we have studied the interaction between StOASS-A, a potential antibacterial target, and an inhibitor previously reported. This approach allowed us to consider the ligand-target complex from a dynamic point of view, revealing the presence of an accessory subpocket which can be exploited to design novel StOASS-A inhibitors. As a proof of concept, a small library of derivatives was designed and evaluated in vitro, displaying the expected activity.

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Section: Introductionmentioning

confidence: 99%

Integration of Enhanced Sampling Methods with Saturation Transfer Difference Experiments to Identify Protein Druggable Pockets

Magalhães¹,

Annunziato²,

Franko³

et al. 2018

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Combining multiple scoring functions can generate proposals close to the query, while repeated runs explore different chemical regions. The main difference between normalized sums or products of scores is the importance to individual violations. , For a normalized sum, each score contributes by a fixed proportion to the total score. Therefore, if three out of four scores are high (close to 1), the total score will be high as well.…”

Section: Resultsmentioning

confidence: 99%

Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

et al. 2020

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Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure−activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.

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“…[a,b] The first value indicates the median value derived for a set of 119 marked CNS drugs and a set of 108 Pfizer CNS clinical candidates, respectively; numbers in brackets are the range values for the same set. [c] Suggested values for increasing the potential for blood–brain barrier (BBB) penetration and oral bioavailability ,. [d] Calculated octanol/water partition coefficient.…”

Section: Figurementioning

confidence: 99%

A Small‐Molecule Inhibitor of Prion Replication and Mutant Prion Protein Toxicity

et al. 2017

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Into the fold: Prion diseases are neurodegenerative disorders characterized by the accumulation in the brain of a self‐replicating, misfolded isoform (PrPSc) of the cellular prion protein (PrPC). No therapies are available for these pathologies. We capitalized on previously described cell‐based assays to screen a library of small molecules, and identified 55, a compound capable of counteracting both prion replication and toxicity. Compound 55 may represent the starting point for the development of a completely new class of therapeutics for prion diseases.

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Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions

Cited by 19 publications

References 17 publications

Integration of Enhanced Sampling Methods with Saturation Transfer Difference Experiments to Identify Protein Druggable Pockets

Integration of Enhanced Sampling Methods with Saturation Transfer Difference Experiments to Identify Protein Druggable Pockets

Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

A Small‐Molecule Inhibitor of Prion Replication and Mutant Prion Protein Toxicity

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