Fragment-based QSAR strategies in drug design

Evaluating the Therapeutic Potential of Curcumin and Synthetic Derivatives: A Computational Approach to Anti-Obesity Treatments

Natural compounds such as curcumin, a polyphenolic compound derived from the rhizome of turmeric, have gathered remarkable scientific interest due to their diverse metabolic benefits including anti-obesity potential. However, curcumin faces challenges stemming from its unfavorable pharmacokinetic profile. To address this issue, synthetic curcumin derivatives aimed at enhancing the biological efficacy of curcumin have previously been developed. In silico modelling techniques have gained significant recognition in screening synthetic compounds as drug candidates. Therefore, the primary objective of this study was to assess the pharmacokinetic and pharmacodynamic characteristics of three synthetic derivatives of curcumin. This evaluation was conducted in comparison to curcumin, with a specific emphasis on examining their impact on adipogenesis, inflammation, and lipid metabolism as potential therapeutic targets of obesity mechanisms. In this study, predictive toxicity screening confirmed the safety of curcumin, with the curcumin derivatives demonstrating a safe profile based on their LD50 values. The synthetic curcumin derivative 1A8 exhibited inactivity across all selected toxicity endpoints. Furthermore, these compounds were deemed viable candidate drugs as they adhered to Lipinski's rules and exhibited favorable metabolic profiles. Molecular docking studies revealed that both curcumin and its synthetic derivatives exhibited favorable binding scores, whilst molecular dynamic simulations showed stable binding with peroxisome proliferator-activated receptor gamma (PPARγ), csyclooxygenase-2 (COX2), and fatty acid synthase (FAS) proteins. The binding free energy calculations indicated that curcumin displayed potential as a strong regulator of PPARγ (-60.2 ± 0.4 kcal/mol) and FAS (-37.9 ± 0.3 kcal/mol), whereas 1A8 demonstrated robust binding affinity with COX2 (-64.9 ± 0.2 kcal/mol). In conclusion, the results from this study suggest that the three synthetic curcumin derivatives have similar molecular interactions to curcumin with selected biological targets. However, in vitro and in vivo experimental studies are recommended to validate these findings.

Design and Evaluation of Synthesized Pyrrole Derivatives as Dual COX-1 and COX-2 Inhibitors Using FB-QSAR Approach

This study delves into the intricate dynamics of the inflammatory response, unraveling the pivotal role played by cyclooxygenase (COX) enzymes, particularly COX-1 and COX-2 subtypes. Motivated by the pursuit of advancing scientific knowledge, our contribution to this field is marked by the design and synthesis of novel pyrrole derivatives. Crafted as potential inhibitors of COX-1 and COX-2 enzymes, our goal was to unearth molecules with heightened efficacy in modulating enzyme activity. A meticulous exploration of a synthesis library, housing around 3000 compounds, expedited the identification of potent candidates. Employing advanced docking studies and field-based Quantitative Structure-Activity Relationship (FB-QSAR) analyses enriched our understanding of the complex interactions between synthesized compounds and COX enzymes. Guided by FB-QSAR insights, our synthesis path led to the identification of compounds 4g, 4h, 4l, and 4k as potent COX-2 inhibitors, surpassing COX-1 efficacy. Conversely, compounds 5b and 5e exhibited heightened inhibitory activity against COX-1 relative to COX-2. The utilization of pyrrole derivatives as COX enzyme inhibitors holds promise for groundbreaking advancements in the domain of anti-inflammatory therapeutics, presenting avenues for innovative pharmaceutical exploration.

Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks

Current practice of building QSAR models usually involves computing a set of descriptors for the training set compounds, applying a descriptor selection algorithm and finally using a statistical fitting method to build the model. In this study, we explored the prospects of building good quality interpretable QSARs for big and diverse datasets, without using any pre-calculated descriptors. We have used different forms of Long Short-Term Memory (LSTM) neural networks to achieve this, trained directly using either traditional SMILES codes or a new linear molecular notation developed as part of this work. Three endpoints were modeled: Ames mutagenicity, inhibition of P. falciparum Dd2 and inhibition of Hepatitis C Virus, with training sets ranging from 7,866 to 31,919 compounds. To boost the interpretability of the prediction results, attention-based machine learning mechanism, jointly with a bidirectional LSTM was used to detect structural alerts for the mutagenicity data set. Traditional fragment descriptor-based models were used for comparison. As per the results of the external and cross-validation experiments, overall prediction accuracies of the LSTM models were close to the fragment-based models. However, LSTM models were superior in predicting test chemicals that are dissimilar to the training set compounds, a coveted quality of QSAR models in real world applications. In summary, it is possible to build QSAR models using LSTMs without using pre-computed traditional descriptors, and models are far from being "black box." We wish that this study will be helpful in bringing large, descriptor-less QSARs to mainstream use.

Distributed Representation of Chemical Fragments

This article describes an unsupervised machine learning method for computing distributed vector representation of molecular fragments. These vectors encode fragment features in a continuous high-dimensional space and enable similarity computation between individual fragments, even for small fragments with only two heavy atoms. The method is based on a word embedding algorithm borrowed from natural language processing field, and approximately 6 million unlabeled PubChem chemicals were used for training. The resulting dense fragment vectors are in contrast to the traditional sparse "one-hot" fragment representation and capture rich relational structure in the fragment space. The vectors of small linear fragments were averaged to yield distributed vectors of bigger fragments and molecules, which were used for different tasks, e.g., clustering, ligand recall, and quantitative structure-activity relationship modeling. The distributed vectors were found to be better at clustering ring systems and recall of kinase ligands as compared to standard binary fingerprints. This work demonstrates unsupervised learning of fragment chemistry from large sets of unlabeled chemical structures and subsequent application to supervised training on relatively small data sets of labeled chemicals.

Mechanistic applications of click chemistry for pharmaceutical drug discovery and drug delivery

The concept of click chemistry (CC), first introduced by K.B. Sharpless, has been widely adopted for use in drug discovery, novel drug delivery systems (DDS), polymer chemistry, and material sciences. In this review, we outline novel aspects of CC related to drug discovery and drug delivery, with a brief overview of molecular mechanisms underlying each click reaction commonly used by researchers, and the main patents that paved the way for further diverse medicinal applications. We also describe recent progress in drug discovery and polymeric and carbon material-based drug delivery for potential pharmaceutical applications and advancements based on the CC approach, and discuss some intrinsic limitations of this popular conjugation reaction. The use of CC is likely to significantly advance drug discovery and bioconjugation development.

Understanding PPAR-δ affinity and selectivity using hologram quantitative structure-activity modeling, molecular docking and GRID calculations

AIM

Type 2 diabetes mellitus and metabolic syndrome are two diseases related to disorders of lipid and carbohydrate metabolism and insulin resistance. Peroxisome proliferator-activated receptors (PPARs) are a class of nuclear receptors that control the metabolism of lipids/carbohydrates and are considered targets for both diseases. PPAR affinity and selectivity are critical points to design drug candidates with appropriated pharmacodynamic/kinetic profiles.

MATERIALS & METHODS

Hologram quantitative structure-activity relationships studies were conducted, as well molecular docking and molecular interaction field calculations, in order to explain affinity and selectivity of selected compounds.

RESULTS

The constructed hologram quantitative structure-activity relationship models are robust and predictive (values of q² and r²_test above 0.70).

CONCLUSION

The quantitative structure-activity relationship models and docking/GRID analyses indicated that carboxyl group of indole-sulfonamide derivatives could interact at helix-3 region, being considered important point of PPAR-δ selectivity.

Hologram QSAR studies of antiprotozoal activities of sesquiterpene lactones

Infectious diseases such as trypanosomiasis and leishmaniasis are considered neglected tropical diseases due the lack for many years of research and development into new drug treatments besides the high incidence of mortality and the lack of current safe and effective drug therapies. Natural products such as sesquiterpene lactones have shown activity against T. brucei and L. donovani, the parasites responsible for these neglected diseases. To evaluate structure activity relationships, HQSAR models were constructed to relate a series of 40 sesquiterpene lactones (STLs) with activity against T. brucei, T. cruzi, L. donovani and P. falciparum and also with their cytotoxicity. All constructed models showed good internal (leave-one-out q² values ranging from 0.637 to 0.775) and external validation coefficients (r²_test values ranging from 0.653 to 0.944). From HQSAR contribution maps, several differences between the most and least potent compounds were found. The fragment contribution of PLS-generated models confirmed the results of previous QSAR studies that the presence of α,β-unsatured carbonyl groups is fundamental to biological activity. QSAR models for the activity of these compounds against T. cruzi, L. donovani and P. falciparum are reported here for the first time. The constructed HQSAR models are suitable to predict the activity of untested STLs.

Consensus ranking approach to understanding the underlying mechanism with QSAR

Constructing a highly predictive model and exploiting the underlying mechanism associated with a specific property of chemicals are the two main goals of quantitative structure-activity relationship analysis (QSAR). However, the latter has long been carried out as a byproduct of model construction. Here we confirmed for the first time in this study that conventional descriptor selection methods designed to develop a best predictive model are likely not suitable for mechanistic analysis, i.e., the selected descriptors strongly depended on the selection of chemicals in the training sets. As an alternative, a consensus ranking protocol was proposed to select a robust descriptor set for mechanistic analysis, which can successfully overcome the above shortcoming. Moreover, the consistently inferior model performance using descriptors selected for mechanistic analysis suggested the irreplaceable role of model development in achieving models with the best predictive capability.