Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research DOI Creative Commons
Valeria V. Kleandrova, M. Natália D. S. Cordeiro, Alejandro Speck‐Planche

et al.

Current Issues in Molecular Biology, Journal Year: 2025, Volume and Issue: 47(5), P. 301 - 301

Published: April 25, 2025

Cancers constitute a group of biological complex diseases, which are associated with great prevalence and mortality. These medical conditions very difficult to tackle due their multi-factorial nature, includes ability evade the immune system become resistant current anticancer agents. There is pressing need search for novel agents multi-target modes action and/or multi-cell inhibition versatility, can translate into more efficacious safer chemotherapeutic treatments. Computational methods paramount importance accelerate drug discovery in cancer research but most them have several disadvantages such as use limited structural information through homogeneous datasets chemicals, prediction activity against single target, lack interpretability. This mini-review discusses emergence, development, application perturbation-theory machine learning (PTML) cutting-edge approach capable overcoming aforementioned limitations context small molecule discovery. Here, we analyze promising investigations on PTML modeling spanning over decade enable versatile We highlight potential while envisaging future applications modeling.

Language: Английский

A Review on Revolutionizing Healthcare Technologies with AI and ML Applications in Pharmaceutical Sciences DOI Creative Commons
Priyanka Kandhare, Mrunal Kurlekar,

Tanvi Deshpande

et al.

Drugs and Drug Candidates, Journal Year: 2025, Volume and Issue: 4(1), P. 9 - 9

Published: March 4, 2025

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research development is transforming the industry by improving efficiency effectiveness across drug discovery, development, healthcare delivery. This review explores diverse applications AI ML, emphasizing their role predictive modeling, repurposing, lead optimization, clinical trials. Additionally, highlights AI’s contributions to regulatory compliance, pharmacovigilance, personalized medicine while addressing ethical considerations. Methods: A comprehensive literature was conducted assess impact ML various domains. Research articles, case studies, reports were analyzed examine AI-driven advancements computational chemistry, trials, safety, supply chain management. Results: have demonstrated significant research, including improved target identification, accelerated discovery through generative models, enhanced structure-based design via molecular docking QSAR modeling. In streamlines patient recruitment, predicts trial outcomes, enables real-time monitoring. maintenance, process inventory management manufacturing chains. Furthermore, has revolutionized enabling precise treatment strategies genomic data analysis, biomarker diagnostics. Conclusions: are reshaping offering innovative solutions care. enhances outcomes operational efficiencies raising challenges that require transparent, accountable applications. Future will rely on collaborative efforts ensure its responsible implementation, ultimately driving continued transformation sector.

Language: Английский

Citations

0
Ensemble Techniques for Predictive Modeling of Leishmanial Activity via Molecular Fingerprints DOI
Saif Nalband,

Pallavi Kiratkar,

Ghanshyam Das Gupta

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Abstract Background:Leishmaniasis, a neglected tropical disease caused by Leishmania protozoan parasites and transmitted sandflies, poses significant global health challenge, especially in resource-limited environments. The life cycle of the parasite includes crucial amastigote promastigote stages, each contributing importantly to infection process. current therapies for leishmaniasis face limitations due considerable side effects rise drug-resistant strains, underscoring pressing need new, effective, safe treatment options. \textcolor{red}{Recent advancements vaccine development include live attenuated vaccines, recombinant use synthetic biology. These approaches aim induce robust immune responses while ensuring safety. Controlled human studies are also being explored accelerate development. However, licensed remains elusive.} Method:This study introduces novel method drug discovery targeting leishmaniasis, employing machine learning cheminformatics forecast efficacy compounds against promastigotes. A detailed dataset consisting 65,057 molecules sourced from PubChem database is utilized, with Alamar Blue-based assay applied assess susceptibility. data encoding relies on molecular fingerprints derived Simplified Molecular Input Line Entry System (SMILES) notations. We employed three distinct fingerprint algorithms, Avalon, MACCS Key, Pharmacophore, models. Various including random forest, multilayer perceptron, gradient boosting, decision tree, utilized create models that effectively classify as either active or inactive based their structural chemical characteristics, which could significantly impact process leishmaniasis. Results: additionally introduced model ensembles, achieving peak accuracy 83.65% an area under curve 0.8367. This offers promise enhancing efforts focused tackling issue Conclusion: Furthermore, proposed approach has potential serve framework addressing other overlooked diseases, offering promising alternative conventional methods associated difficulties.

Language: Английский

Citations

0
Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research DOI Creative Commons
Valeria V. Kleandrova, M. Natália D. S. Cordeiro, Alejandro Speck‐Planche

et al.

Current Issues in Molecular Biology, Journal Year: 2025, Volume and Issue: 47(5), P. 301 - 301

Published: April 25, 2025

Cancers constitute a group of biological complex diseases, which are associated with great prevalence and mortality. These medical conditions very difficult to tackle due their multi-factorial nature, includes ability evade the immune system become resistant current anticancer agents. There is pressing need search for novel agents multi-target modes action and/or multi-cell inhibition versatility, can translate into more efficacious safer chemotherapeutic treatments. Computational methods paramount importance accelerate drug discovery in cancer research but most them have several disadvantages such as use limited structural information through homogeneous datasets chemicals, prediction activity against single target, lack interpretability. This mini-review discusses emergence, development, application perturbation-theory machine learning (PTML) cutting-edge approach capable overcoming aforementioned limitations context small molecule discovery. Here, we analyze promising investigations on PTML modeling spanning over decade enable versatile We highlight potential while envisaging future applications modeling.

Language: Английский

Citations

0