QSAR-Assisted Virtual Screening of Lead-Like Molecules from Marine and Microbial Natural Sources for Antitumor and Antibiotic Drug Discovery

Pereira, Florbela; Latino, Diogo A. R. S.; Gaudêncio, Susana P.

doi:10.3390/molecules20034848

Cited by 26 publications

(24 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, Log P is one of the most important molecular descriptors since it is highly correlated with lipophilicity, thus, more lipophilic molecules are often discontinued from drug development and are frequently connected to toxicity issues . Besides MW and Log P, we recently reported that the topological descriptor MDEO‐12 (molecular distance edge between primary and secondary oxygen atoms), the electronic descriptor TopoPSA (topological polar surface area) and the quantum‐chemical descriptor HOMO (highest occupied molecular orbital energy), have a remarkable performance in discriminating antitumor, antibiotic and overall biological lead‐like compounds, respectively.…”

Section: Resultssupporting

confidence: 78%

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel K_V1.3 Inhibitors

et al. 2018

Self Cite

View full text Add to dashboard Cite

Voltage‐gated ion channels are key molecular targets for autoimmune diseases such as multiple sclerosis, rheumatoid arthritis and psoriasis. In silico models, using 340 molecules whose IC50 towards Kv1.3 was determined by reported assays, were developed through exploration of four machine learning (ML) techniques. ML techniques explored included Random Forest, Support Vector Machine, Multilayer Perceptron, and K‐Nearest Neighbors. Two QSAR classification approaches were developed. In the first approach, the compounds were classified into either moderate‐active‐to‐very‐active or inactive‐to‐moderate‐active categories. Only the compounds predicted to be moderate‐active‐to‐very‐active in the first classification model were submitted to the second model, a classification model that predicted two more categories, very‐active and not‐very‐active. The performances of the models were successfully evaluated by internal validation and external test set validation, with an overall predictability (Q) of 0.83 and 0.69 for the test set in the first and second approaches of the best models, respectively. The best models for the two approaches were employed for the virtual screening of KV1.3 inhibitors from ZINC natural products and approved drugs databases. A list of the most promising lead‐like KV1.3 inhibitors was proposed, from which an approved drug and a natural product were experimentally evaluated with whole‐cell voltage‐clamp assays at 1000 nM.

show abstract

Section: Resultssupporting

confidence: 78%

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel K_V1.3 Inhibitors

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, LogP is one of the most important molecular descriptors since it is highly correlated with lipophilicity, thus, more lipophilic molecules are often discontinued from drug development and are frequently related to toxicity issues [ 45 ]. Besides MW and LogP, we recently reported that the topological descriptor MDEO-12 (molecular distance edge between primary and secondary oxygen atoms) [ 46 ], the electronic descriptor TopoPSA (topologicalpolar surface area) [ 46 ] and the quantum-chemical descriptor HOMO (highest occupied molecular orbital energy) [ 47 ], have a remarkable performance in discriminating antitumor, antibiotic and overall biological lead-like compounds, respectively.…”

Section: Resultsmentioning

confidence: 99%

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs

et al. 2018

Self Cite

View full text Add to dashboard Cite

To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.

show abstract

“…Consequently, some research groups focus their work on the discovery of antibiotics in nature. [70][71][72] Thai et al have developed a linear QSAR model to analyze a collection of 182 flavonoids and the Chinese Medicine Database searching for new S. aureus NorA pump inhibitors. 70 Similarly, Pereira et al developed a QSAR model for the search of antibacterial compounds in PubChem (1,804 compounds) and the AntiMarin Database (418 compounds).…”

Section: Qsar In Antibacterial Compound Developmentmentioning

confidence: 99%

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Suay-García

Bueso-Bordils

Falcó

et al. 2020

WIREs Comput Mol Sci

View full text Add to dashboard Cite

With the pressing issue of antibiotic resistance, there is a constant need for new antibiotics. However, the fact that traditional methods of drug discovery are expensive and time-consuming has discouraged the pharmaceutical industry, leaving the burden of discovery to research institutions. This is where quantitative structure-activity relationship (QSAR) methods become a key tool in fighting multidrug-resistant bacteria, seeing as they provide useful information for the rational design of new active molecules at a minimal cost. A variety of linear and nonlinear statistical methods are used to develop these models based on the 2D or 3D representations of the molecules. QSAR models have proven to be effective in rapidly providing lead compound candidates against resistant bacteria such as methicillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas spp., Bacillus subtilis, or Mycobacterium tuberculosis. Moreover, QSAR methods allow for a deeper analysis of a library of molecules, selecting those with not only the optimal activity, but also the most favorable pharmacokinetic and toxicological profiles. The information obtained from QSAR studies makes optimizing an existing drug simpler, which is a cost-effective approach to obtain new treatments against increasingly resistant bacteria.

show abstract

QSAR-Assisted Virtual Screening of Lead-Like Molecules from Marine and Microbial Natural Sources for Antitumor and Antibiotic Drug Discovery

Cited by 26 publications

References 53 publications

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel K_V1.3 Inhibitors

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel K_V1.3 Inhibitors

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Contact Info

Product

Resources

About

QSAR-Assisted Virtual Screening of Lead-Like Molecules from Marine and Microbial Natural Sources for Antitumor and Antibiotic Drug Discovery

Cited by 26 publications

References 53 publications

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel KV1.3 Inhibitors

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel KV1.3 Inhibitors

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Contact Info

Product

Resources

About

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel K_V1.3 Inhibitors

A Ligand‐Based Approach to the Discovery of Lead‐Like Potassium Channel K_V1.3 Inhibitors