COMPUTER-AIDED DRUG DISCOVERY: FROM TARGET PROTEINS TO DRUG CANDIDATES

1998 ◽  
Author(s):  
J. Bajorath ◽  
T. E. Klein ◽  
T. P. Lybrand ◽  
J. Novotny
Keyword(s):  
Drug Discovery ◽  
Target Proteins ◽  
Drug Candidates ◽  
Computer Aided

scholarly journals Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
José T. Moreira-Filho ◽  
Arthur C. Silva ◽  
Rafael F. Dantas ◽  
Barbara F. Gomes ◽  
Lauro R. Souza Neto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Cost Effective ◽  
Neglected Tropical Disease ◽  
Parasitic Disease ◽  
Drug Candidates ◽  
Single Drug ◽  
Computer Aided ◽  
Prevalent Disease ◽  
Effective Drugs

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

scholarly journals Computer-aided drug discovery

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 630 ◽  
Author(s):  
Jürgen Bajorath
Keyword(s):  
Drug Discovery ◽  
Computational Tools ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Discovery Research ◽  
Current Trends ◽  
Drug Discovery Research ◽  
Computer Aided ◽  
Computational Drug Discovery ◽  
Different Levels

Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

New Perspectives of Machine Learning in Drug Discovery

2020 ◽  
Vol 27 ◽  
Author(s):  
Simona Musella ◽  
Giulio Verna ◽  
Alessio Fasano ◽  
Simone Di Micco
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Structural Design ◽  
Data Sets ◽  
Future Perspectives ◽  
Drug Candidates ◽  
Computer Aided ◽  
Information Interpretation ◽  
The Impact ◽  
Complex Manner

: Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to clinical trial. This approach is harnessing the impact of computer-aided drug discovery thanks to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

2020 ◽  
Vol 21 (10) ◽  
pp. 751-767
Author(s):  
Pobitra Borah ◽  
Sangeeta Hazarika ◽  
Satyendra Deka ◽  
Katharigatta N. Venugopala ◽  
Anroop B. Nair ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Natural Product ◽  
Dynamic Range ◽  
Early Stage ◽  
Attrition Rate ◽  
Drug Candidates ◽  
Advanced Technologies ◽  
Natural Product Research ◽  
The Status ◽  
Safety Parameters

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Current Approaches to Drug Discovery for Chagas Disease: Methodological Advances

2019 ◽  
Vol 22 (8) ◽  
pp. 509-520
Author(s):  
Cauê B. Scarim ◽  
Chung M. Chin
Keyword(s):  
Drug Discovery ◽  
Chagas Disease ◽  
Drug Candidates ◽  
Lead Compounds ◽  
New Drug ◽  
Compound Libraries ◽  
Screening Assays ◽  
Cruzi Infection

Background: In recent years, there has been an improvement in the in vitro and in vivo methodology for the screening of anti-chagasic compounds. Millions of compounds can now have their activity evaluated (in large compound libraries) by means of high throughput in vitro screening assays. Objective: Current approaches to drug discovery for Chagas disease. Method: This review article examines the contribution of these methodological advances in medicinal chemistry in the last four years, focusing on Trypanosoma cruzi infection, obtained from the PubMed, Web of Science, and Scopus databases. Results: Here, we have shown that the promise is increasing each year for more lead compounds for the development of a new drug against Chagas disease. Conclusion: There is increased optimism among those working with the objective to find new drug candidates for optimal treatments against Chagas disease.

Targeted Protein Degradation: "The Gold Rush is On!"

2020 ◽  
Vol 7 (1) ◽  
pp. 4-16
Author(s):  
Daria Kotlarek ◽  
Agata Pawlik ◽  
Maria Sagan ◽  
Marta Sowała ◽  
Alina Zawiślak-Architek ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Small Molecule ◽  
Gold Rush ◽  
European Company ◽  
Drug Candidates ◽  
Protein Inhibition ◽  
Small Molecule Drug ◽  
Therapeutic Benefits ◽  
Talent Pool

Targeted Protein Degradation (TPD) is an emerging new modality of drug discovery that offers unprecedented therapeutic benefits over traditional protein inhibition. Most importantly, TPD unlocks the untapped pool of the proteome that to date has been considered undruggable. Captor Therapeutics (Captor) is the fourth global, and first European, company that develops small molecule drug candidates based on the principles of targeted protein degradation. Captor is located in Basel, Switzerland and Wroclaw, Poland and exploits the best opportunities of the two sites – experience and non-dilutive European grants, and talent pool, respectively. Through over $38 M of funding, Captor has been active in three areas of TPD: molecular glues, bi-specific degraders and direct degraders, ObteronsTM.

scholarly journals Prospect into therapeutic potentials of Moringa oleifera phytocompounds against cancer upsurge: de novo synthesis of test compounds, molecular docking, and ADMET studies

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
P. M. Aja ◽  
P. C. Agu ◽  
E. M. Ezeh ◽  
J. N. Awoke ◽  
H. A. Ogwoni ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Cancer Chemotherapy ◽  
Moringa Oleifera ◽  
De Novo ◽  
Gas Chromatography Mass Spectrometry ◽  
Molecular Docking Study ◽  
Cancer Proliferation ◽  
Target Proteins ◽  
Potential Inhibitors

Abstract Background Cancer chemotherapy is difficult because current medications for the treatment of cancer have been linked to a slew of side effects; as a result, researchers are tasked with developing greener cancer chemotherapies. Moringa oleifera has been reported with several bioactive compounds which confirm its application for various ailments by traditional practitioners. In this study, we aim to prospect the therapeutic potentials of M. oleifera phytocompounds against cancer proliferation as a step towards drug discovery using a computational approach. Target proteins: dihydrofolate reductase (DHFR) and B-Cell Lymphoid-2 (BCL-2), were retrieved from the RCSB PDB web server. Sixteen and five phytocompounds previously reported in M. oleifera leaves (ML) and seeds (MS), respectively, by gas chromatography–mass spectrometry were synthesized and used in the molecular docking study. For accurate prediction of binding sites of the target proteins; standard inhibitors, Methotrexate (MTX) for DHFR, and Venetoclax (VTC) for BCL-2, were docked together with the test compounds. We further predicted the ADMET profile of the potential inhibitors for an insight into their chance of success as candidates in drug discovery. Results Results for the binding affinities, docking poses, and the interactions showed that ML2, ML4-6, ML8-15, and MS1-5 are potential inhibitors of DHFR and BCL-2, respectively. In the ADMET profile, ML2 and ML4 showed the best drug-likeness by non-violation of Lipski Rule of Five. ML4-6, ML8, ML11, ML14-15, and MS1, MS3-5 exhibit high GI absorption; ML2, ML4-6, ML8, MS1, and MS5 are blood–brain barrier permeants. ML2, ML4, ML9, ML13, and MS2 do not interfere with any of the CYP450 isoforms. The toxicity profile showed that all the potential inhibitors are non-carcinogenic and non-hERG I (human ether-a-go-go related gene I) inhibitors. ML4, ML11, and MS4 are hepatotoxic and ML7, ML10, and MS4 are hERG II inhibitors. A plethora of insights on the toxic endpoints and lethal concentration values showed that ML5, ML13, and MS2 are comparatively less lethal than other potential inhibitors. Conclusion This study has demonstrated that M. oleifera phytocompounds are potential inhibitors of the disease proteins involved in cancer proliferation, thus, an invaluable step toward the discovery of cancer chemotherapy with lesser limitations.

Applications of Computer-Aided Pharmacokinetic and Pharmacodynamic Methods from Drug Discovery Through Registration

2008 ◽  
Vol 4 (1) ◽  
pp. 54-66 ◽  
Author(s):  
John Gibbs ◽  
Jennifer Dong ◽  
Bin Chen ◽  
Megan Gibbs ◽  
Maurice Emery
Keyword(s):  
Drug Discovery ◽  
Computer Aided

scholarly journals SELDI ProteinChip®Array Technology: Protein-Based Predictive Medicine and Drug Discovery Applications

2003 ◽  
Vol 2003 (4) ◽  
pp. 237-241 ◽  
Author(s):  
Guru Reddy ◽  
Enrique A. Dalmasso
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Treatment Options ◽  
Drug Effects ◽  
Proteome Analysis ◽  
Predictive Medicine ◽  
Protein Drug ◽  
Proteinchip Array ◽  
Drug Candidates ◽  
Array Technology

Predictive medicine, utilizing the ProteinChip®Array technology, will develop through the implementation of novel biomarkers and multimarker patterns for detecting disease, determining patient prognosis, monitoring drug effects such as efficacy or toxicity, and for defining treatment options. These biomarkers may also serve as novel protein drug candidates or protein drug targets. In addition, the technology can be used for discovering small molecule drugs or for defining their mode of action utilizing protein-based assays. In this review, we describe the following applications of the ProteinChip Array technology: (1) discovery and identification of novel inhibitors of HIV-1 replication, (2) serum and tissue proteome analysis for the discovery and development of novel multimarker clinical assays for prostate, breast, ovarian, and other cancers, and (3) biomarker and drug discovery applications for neurological disorders.

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