Biology subjects

Merritt, A.

Publications and source records attributed to Merritt, A..

3 recordsLinked to original sources

bioRxiv2022

The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges

AO_SCPLOWBSTRACTC_SCPLOWFragment screening using X-ray crystallography can yield rich structural data to help guide the optimization of low-molecular-weight compounds into more potent binders. Fragment merging, whereby substructural motifs from partially overlapping fragments are incorporated into a single larger compound, represents a potentially powerful and efficient approach for increasing potency. Searching commercial catalogues provides one useful way to quickly and cheaply identify follow-up compounds for purchase and further screening, and circumvents the challenge of synthetic accessibility. The Fragment Network is a graph database that provides a novel way to explore the chemical space surrounding fragment hits. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four XChem fragment screening campaigns. Retrieved molecules were filtered using a pipeline of 2D and 3D filters and contrasted against a traditional fingerprint-based similarity search. The two search techniques were found to have complementary results, identifying merges in different regions of chemical space. Both techniques were able to identify merges that are predicted to replicate the interactions made by the parent fragments. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search, thus increasing the likelihood of finding promising follow-up compounds. We present a pipeline that is able to systematically exploit all known fragment hits by performing large-scale enumeration of all possible fragment pairs for merging.

bioinformatics↗

bioRxiv2021

Affinity Scores: An Individual-centric Fingerprinting Framework for Neuropsychiatric Disorders

BackgroundPopulation-centric frameworks of biomarker identification for psychiatric disorders focus primarily on comparing averages between groups and assume that diagnostic groups are (1) mutually-exclusive, and (2) homogeneous. There is a paucity of individual-centric approaches capable of identifying individual-specific fingerprints across multiple domains. To address this, we propose a novel framework, combining a range of biopsychosocial markers, including brain structure, cognition, and clinical markers, into higher-level fingerprints, capable of capturing intra-illness heterogeneity and inter-illness overlap. MethodsA multivariate framework was implemented to identify individualised patterns of brain structure, cognition and clinical markers based on affinity to other participants in the database. First, individual-level affinity scores defined each participants "neighbourhood" across each measure based on variable-specific hop sizes. Next, diagnostic verification and classification algorithms were implemented based on multivariate affinity score profiles. To perform affinity-based classification, data were divided into training and test samples, and 5-fold nested cross-validation was performed on the training data. Affinity-based classification was compared to weighted K-nearest neighbours (KNN) classification. K-means clustering was used to create clusters based on multivariate affinity score profiles. The framework was applied to the Australian Schizophrenia Research Bank (ASRB) dataset. ResultsIndividualised affinity scores provided a fingerprint of brain structure, cognition, and clinical markers, which described the affinity of an individual to the representative groups in the dataset Diagnostic verification capability was moderate to high depending on the choice of multivariate affinity metric. Affinity score-based classification achieved a high degree of accuracy in the training, nested cross-validation and prediction steps, and outperformed KNN classification in the training and test datasets. ConclusionAffinity scores demonstrate utility in two keys ways: (1) Early and accurate diagnosis of neuropsychiatric disorders, whereby an individual can be grouped within a diagnostic category/ies that best matches their fingerprint, and (2) identification of biopsychosocial factors that most strongly characterise individuals/disorders, and which may be most amenable to intervention.

neuroscience↗

bioRxiv2021

Incorporating Target-Specific Pharmacophoric Information Into Deep Generative Models For Fragment Elaboration

Despite recent interest in deep generative models for scaffold elaboration, their applicability to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability to account for local protein structure or a users design hypothesis. We propose a novel method for fragment elaboration, STRIFE that overcomes these issues. STRIFE takes as input Fragment Hotspot Maps (FHMs) extracted from a protein target, and processes them to provide meaningful and interpretable structural information to its generative model, which in turn is able to rapidly generate elaborations with complementary pharmacophores to the protein. In a large-scale evaluation, STRIFE outperforms existing, structure-unaware, fragment elaboration methods in proposing highly ligand efficient elaborations. In addition to automatically extracting pharmacophoric information from a protein targets FHM, STRIFE optionally allows the user to specify their own design hypotheses.

systems biology↗