Search arXivSearch

arXiv · 2412.11137

Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners

Abstract

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz, Bryar A. Hassan, Tarik A. Rashid, Arif Kivrak. 2024-12-15. Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners. https://doi.org/10.1007/s12010-024-05110-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup

An individual's adaptive immune receptor (AIR) repertoire records immune history due to the receptors' antigen specificity. Reading this record requires computational approaches for inferring receptor function from sequence, as the diversity of possible receptor-antigen pairs vastly outstrips experimental knowledge. Identification of AIRs with similar sequence and thus putatively similar function is a common performance bottleneck in these approaches. Here, we benchmark the runtime scaling of five algorithmic approaches to radius-based search for Levenshtein neighbors. We show that a symmetric deletion lookup approach, originally proposed for spell-checking, is particularly scalable. We introduce SymScan and XTNeighbor, optimized CPU and GPU software implementing a parallelized variant of the algorithm. For one million input sequences, these tools identify all sequence pairs that differ by one or two edits in seconds, orders of magnitude faster than existing approaches. We demonstrate how symmetric deletion lookup can be integrated as a pre-filtering step in T cell receptor metaclone discovery and B cell receptor lineage identification. Our contribution is poised to greatly accelerate existing analysis pipelines and enable processing of immunosequencing data at scale.

q-bio.QM

Implication of modelling choices on connectivity estimation: A comparative analysis

Landscape connectivity is an important field with important conservation implications. Connectivity modelling is a useful tool to inform and guide landscape planning. However, it involves assumptions and methodological decisions which ultimately impact connectivity outcomes. In order to understand the implications of modelling choices on final connectivity estimations, we compare two landscape characterisation approaches - expert knowledge and species distribution models - and three movements models - least-cost paths, circuit theory and an individual-based movement simulator. The implementation of the models and the construction of the analyses scope highlighted conceptual and methodological differences that made the comparison difficult. Landscape characterisation appears as the principal factor determining connectivity outcomes. Therefore, the confrontation between expert knowledge and species distribution models is critical to leverage points of convergence and complementarity between these two approaches. Conceptual differences between movement models are reported on connectivity map and habitat patch contribution estimations. In the want of data and protocol design specifically to validate connectivity models, approaches that integrate stochastic and behavioural processes, bring a more realistic perspective to connectivity estimation.

q-bio.QM

Triplication: an important component of the modern scientific method

A scientific-study protocol (defined) is designed to deliver results from which inductive inference is allowed. In the nineteenth century, triplication was introduced into the plant sciences and Fisher's p<0.05 rule (1925) incorporated into triple-result protocols designed to counter random/systematic errors which contribute to real-world variability. The aims of the present study were to: (1) classify replication protocols; (2) assess their prevalence in plant-science studies (published during one twenty-first-century year; for defined variable construct); (3) explore triplication rationale. Methods: a plant-sciences protocol-prevalence report was produced; experimental/associational-study proportions analyzed; and real-world-data proxies used to show confidence-interval-width patterns with increasing replicate number. Results: 25% plant-science studies analyzed showed triplication, including 11% triple-result protocols (including greater replicate numbers: 48%;17%, respectively). Theoretical considerations indicated that even if systematic errors predominate, (previously-known) square-root rules sometimes apply, contributing to triplication importance (exemplified by real-world-data proxies). Conclusions: The defined protocols, with minor modifications, should provide the means for assessment of most sciences. Triplication was extensively applied in studies analysed and there are strong methodological reasons why triplication, rather than duplication/quadruplication, is the appropriate standard: triple-result protocols: (a) effectively reduce false positives to acceptable levels; (b) give qualitatively-different information (shape) from duplication; (c) have a large efficiency advantage (concerning confidence-interval widths) over quadruplication. The application of batch replication is not, primarily, a statistical problem and cannot effectively be replaced by simulation.

q-bio.QM