PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute ...bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).
We present MuSSeL, a multifingerprint similarity search algorithm, able to predict putative drug targets for a given query small molecule as well as to return a quantitative assessment of its ...bioactivity in terms of Ki or IC50 values. Predictions are automatically made exploiting a large collection of high quality experimental bioactivity data available from ChEMBL (version 22.1) combining, in a consensus-like approach, predictions resulting from a similarity search performed using 13 different fingerprint definitions. Importantly, the herein proposed algorithm is also effective in detecting and handling activity cliffs. A calibration set including small molecules present in the last updated version of ChEMBL (version 23) was employed to properly tune the algorithm parameters. Three randomly built external sets were instead challenged for model performances. The potential use of MuSSeL was also challenged by a prospective exercise for the prediction of five bioactive compounds taken from articles published in the Journal of Medicinal Chemistry just few months ago. The paper emphasizes the importance of implementing multifingerprint consensus strategies to increase the confidence in prediction of similarity search algorithms and provides a fast and easy-to-run tool for drug target and bioactivity prediction.
•hERG primary anti-target responsible for serious side-effects.•hERG structure–activity relationships, data and models.•hERG channels structure and pathophysiology.•Testing screening methods and CIPA ...guidelines.•hERG target with a role in cancer.
hERG is best known as a primary anti-target, the inhibition of which is responsible for serious side effects. A renewed interest in hERG as a desired target, especially in oncology, was sparked because of its role in cellular proliferation and apoptosis. In this study, we survey the most recent advances regarding hERG by focusing on SAR in the attempt to elucidate, at a molecular level, off-target and on-target actions of potential hERG binders, which are highly promiscuous and largely varying in structure. Understanding the rationale behind hERG interactions and the molecular determinants of hERG activity is a real challenge and comprehension of this is of the utmost importance to prioritize compounds in early stages of drug discovery and to minimize cardiotoxicity attrition in preclinical and clinical studies.
We report how the understanding of SAR and the informed use of reliable data and models are pivotal to modulate the interplay with hERG channels.
In the last decade, selective modulators of type-2 cannabinoid receptor (CB
) have become a major focus to target endocannabinoid signaling in humans. Indeed, heterogeneously expressed within our ...body, CB
actively regulates several physio-pathological processes, thus representing a promising target for developing specific and safe therapeutic drugs. If CB
modulation has been extensively studied since the very beginning for the treatment of pain and inflammation, the more recent involvement of this receptor in other pathological conditions has further strengthened the pursuit of novel CB
agonists in the last five years. Against this background, here we discuss the most recent evidence of the protective effects of CB
against pathological conditions, emphasizing central nervous system disorders, bone and synovial diseases, and cancer. We also summarize the most recent advances in the development of CB
agonists, focusing on the correlation between different chemical classes and diverse therapeutic applications. Data mining includes a review of the CB
ligands disclosed in patents also released in the last five years. Finally, we discuss how the recent elucidation of CB
tertiary structure has provided new details for the rational design of novel and more selective CB
agonists, thus supporting innovative strategies to develop effective therapeutics. Our overview of the current knowledge on CB
agonists provides pivotal information on the structure and function of different classes of molecules and opens possible avenues for future research.
•We provide a critical review of the current state of the activity cliff research.•We comment on and integrate the opinions of experts working on activity cliffs.•The negative effects of activity ...cliffs on prediction models are discussed.•We provide a machine learning rationale for activity cliffs in chemoinformatics.•Potential solutions to address the negative effects of activity cliffs are proposed.
The impact activity cliffs have on drug discovery is double-edged. For instance, whereas medicinal chemists can take advantage of regions in chemical space rich in activity cliffs, QSAR practitioners need to escape from such regions. The influence of activity cliffs in medicinal chemistry applications is extensively documented. However, the ‘dark side’ of activity cliffs (i.e. their detrimental effect on the development of predictive machine learning algorithms) has been understudied. Similarly, limited amounts of work have been devoted to propose potential solutions to the drawbacks of activity cliffs in similarity-based approaches. In this review, the duality of activity cliffs in medicinal chemistry and computational approaches is addressed, with emphasis on the rationale and potential solutions for handling the ‘ugly face’ of activity cliffs.
Point of view of the current state of the activity cliff phenomenon focusing on the rationale, effects and potential solutions to handle the influence of activity cliffs in drug discovery.
In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and ...the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.
The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the ...progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the
-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space.
Previously synthesized novel chalcone oxime ethers (COEs) were evaluated for inhibitory activities against monoamine oxidases (MAOs) and acetylcholinesterase (AChE). Twenty-two of the 24 COEs ...synthesized, except
and
, had potent and/or significant selective inhibitory effects on MAO-B.
potently inhibited MAO-B with an IC
value of 0.018 µM, which was 105, 2.3, and 1.1 times more potent than clorgyline, lazabemide, and pargyline (reference drugs), respectively.
, and
were also active against MAO-B, both had an IC
value of 0.028 µM, which was 67 and 1.5 times lower than those of clorgyline and lazabemide, respectively. Most of the COEs exhibited weak inhibitory effects on MAO-A and AChE.
most potently inhibited MAO-A (IC
= 0.88 µM) and also significantly inhibited MAO-B (IC
= 0.13 µM), and it could be considered as a potential nonselective MAO inhibitor.
and
inhibited AChE with IC
values of 5.35 and 4.39 µM, respectively. The selectivity index (SI) of
for MAO-B was higher than that of
(SI = 778.6 vs. 222.2), but the IC
value (0.028 µM) was slightly lower than that of
(0.018 µM). In reversibility experiments, inhibitions of MAO-B by
and
were recovered to the levels of reference reversible inhibitors and both competitively inhibited MAO-B, with K
values of 0.0075 and 0.010 µM, respectively. Our results show that
and
are potent, selective MAO-B inhibitors, and
is a candidate of dual-targeting molecule for MAO-B and AChE.
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