Computational approaches streamlining drug discovery

Nature, Год журнала: 2023, Номер 616(7958), С. 673 - 685

Опубликована: Апрель 26, 2023

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This is largely defined by flood of data on ligand properties binding to therapeutic targets their 3D structures, abundant computing capacities advent on-demand virtual libraries drug-like small molecules billions. Taking full advantage these resources requires fast methods effective screening. includes structure-based screening gigascale chemical spaces, further facilitated iterative approaches. Highly synergistic are developments deep learning predictions target activities lieu receptor structure. Here we review recent advances technologies, potential reshaping whole process development, as well challenges they encounter. We also discuss how rapid identification highly diverse, potent, target-selective ligands protein can democratize process, presenting new opportunities cost-effective development safer more small-molecule treatments. Recent approaches application streamlining discussed.

Язык: Английский

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AI-powered therapeutic target discovery

Trends in Pharmacological Sciences, Год журнала: 2023, Номер 44(9), С. 561 - 572

Опубликована: Июль 19, 2023

Disease modeling and target identification are the most crucial initial steps in drug discovery, influence probability of success at every step development. Traditional is a time-consuming process that takes years to decades usually starts an academic setting. Given its advantages analyzing large datasets intricate biological networks, artificial intelligence (AI) playing growing role modern identification. We review recent advances focusing on breakthroughs AI-driven therapeutic exploration. also discuss importance striking balance between novelty confidence selection. An increasing number AI-identified targets being validated through experiments several AI-derived drugs entering clinical trials; we highlight current limitations potential pathways for moving forward.

Язык: Английский

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Attention is all you need: utilizing attention in AI-enabled drug discovery

Briefings in Bioinformatics, Год журнала: 2023, Номер 25(1)

Опубликована: Ноя. 22, 2023

Abstract Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance interpretability handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based advantages discovery. We further elaborate on applications various aspects development, from molecular screening target binding property prediction molecule generation. Finally, we discuss current challenges faced application mechanisms Artificial Intelligence technologies, including quality, model computational resource constraints, along with future directions for research. Given accelerating pace technological advancement, believe that will increasingly prominent role anticipate these usher revolutionary breakthroughs pharmaceutical domain, significantly development.

Язык: Английский

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Drug repurposing for cancer therapy

Signal Transduction and Targeted Therapy, Год журнала: 2024, Номер 9(1)

Опубликована: Апрель 18, 2024

Abstract Cancer, a complex and multifactorial disease, presents significant challenge to global health. Despite advances in surgical, radiotherapeutic immunological approaches, which have improved cancer treatment outcomes, drug therapy continues serve as key therapeutic strategy. However, the clinical efficacy of is often constrained by resistance severe toxic side effects, thus there remains critical need develop novel therapeutics. One promising strategy that has received widespread attention recent years repurposing: identification new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages context since repurposed drugs are typically cost-effective, proven be safe, can significantly expedite development process due their already established safety profiles. In light this, present review offers comprehensive overview various methods employed repurposing, specifically focusing on treat cancer. We describe antitumor properties candidate drugs, discuss detail how they target both hallmarks tumor cells surrounding microenvironment. addition, we examine innovative integrating with nanotechnology enhance topical delivery. also emphasize role play when used part combination regimen. To conclude, outline challenges associated consider future prospects these transitioning into application.

Язык: Английский

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Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

Molecules, Год журнала: 2023, Номер 28(2), С. 776 - 776

Опубликована: Янв. 12, 2023

For a new molecular entity (NME) to become drug, it is not only essential have the right biological activity also be safe and efficient, but required favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there need predict, during early stages of development, ADMET properties increase success rate compounds reaching lead optimization process. Since Lipinski's rule five, prediction parameters has evolved towards current in silico tools based on empirical approaches or modeling. The commercial specialized software for performing such predictions, which usually costly, is, many cases, among possibilities research laboratories academia at small biotech companies. Nevertheless, recent years, free online available, allowing, more less accurately, most relevant parameters. This paper studies 18 web servers capable predicting analyzed their advantages disadvantages, model-based calculations, degree accuracy by considering experimental data reported set 24 FDA-approved tyrosine kinase inhibitors (TKIs) as model project.

Язык: Английский

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Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Pharmaceutics, Год журнала: 2024, Номер 16(3), С. 332 - 332

Опубликована: Фев. 27, 2024

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in revolutionary era healthcare by individualizing diagnostics and according to each patient’s uniquely evolving health status. This groundbreaking method tailoring disease prevention treatment considers individual variations genes, environments, lifestyles. goal precision target the “five rights”: right patient, drug, time, dose, route. In this pursuit, silico techniques have emerged as an anchor, driving forward making realistic promising avenue for personalized therapies. With advancements high-throughput DNA sequencing technologies, genomic data, including genetic variants their interactions with other environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) pharmacodynamic (PD) mathematical models further contribute drug optimization, behavior prediction, drug–drug interaction identification. Digital health, wearables, computational tools offer continuous monitoring real-time data collection, enabling adjustments. Furthermore, incorporation extensive datasets tools, such electronic records (EHRs) omics also another pathway acquire meaningful information field. Although they are fairly new, machine learning (ML) algorithms artificial intelligence (AI) resources researchers use analyze big develop predictive models. review explores interplay these multiple approaches advancing fostering healthcare. Despite intrinsic challenges, ethical considerations, protection, need more comprehensive research, marks new patient-centered Innovative hold potential reshape future generations come.

Язык: Английский

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From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples

Molecules, Год журнала: 2022, Номер 27(13), С. 4060 - 4060

Опубликована: Июнь 24, 2022

Ethnopharmacology, through the description of beneficial effects plants, has provided an early framework for therapeutic use natural compounds. Natural products, either in their native form or after crude extraction active ingredients, have long been used by different populations and explored as invaluable sources drug design. The transition from traditional ethnopharmacology to discovery followed a straightforward path, assisted evolution isolation characterization methods, increase computational power, development specific chemoinformatic methods. deriving extensive exploitation product chemical space led novel compounds with pharmaceutical properties, although this was not analogous drugs. In work, we discuss ideas silico discovery, applied products. We point out that, past, starting plant itself, identified sustained ethnopharmacological research, compound analysis testing. contrast, recent years, substance pinpointed methods (in docking molecular dynamics, network pharmacology), identification plant(s) containing ingredient, existing putative information. further stress potential pitfalls absolute need vitro vivo validation requirement. Finally, present our contribution products' discussing examples, applying whole continuum rapidly evolving field. detail, report antiviral compounds, based on products against influenza SARS-CoV-2 substances GPCR, OXER1.

Язык: Английский

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Delivering drugs with microrobots

Science, Год журнала: 2023, Номер 382(6675), С. 1120 - 1122

Опубликована: Дек. 7, 2023

Biomedical microrobots could overcome current challenges in targeted therapies.

Язык: Английский

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Targeting and engineering long non-coding RNAs for cancer therapy

Nature Reviews Genetics, Год журнала: 2024, Номер 25(8), С. 578 - 595

Опубликована: Фев. 29, 2024

Язык: Английский

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Is Target-Based Drug Discovery Efficient? Discovery and “Off-Target” Mechanisms of All Drugs

Journal of Medicinal Chemistry, Год журнала: 2023, Номер 66(18), С. 12651 - 12677

Опубликована: Сен. 6, 2023

Target-based drug discovery is the dominant paradigm of discovery; however, a comprehensive evaluation its real-world efficiency lacking. Here, manual systematic review about 32000 articles and patents dating back to 150 years ago demonstrates apparent inefficiency. Analyzing origins all approved drugs reveals that, despite several decades dominance, only 9.4% small-molecule have been discovered through "target-based" assays. Moreover, therapeutic effects even this minimal share cannot be solely attributed reduced their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based may cause productivity crisis in discovery. An evidence-based approach enhance seems prioritizing, selecting optimizing molecules, higher-level observations are closer sought-after using tools like artificial intelligence machine learning.

Язык: Английский

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