Defining Known Drug Space Using DFT

Latonduine-1-Amino-Hydantoin Hybrid, Triazole-Fused Latonduine Schiff Bases and Their Metal Complexes: Synthesis, X-ray and Electron Diffraction, Molecular Docking Studies and Antiproliferative Activity

A series of latonduine derivatives, namely 11-nitro-indolo[2,3-d]benzazepine-7-(1-amino-hydantoin) (B), triazole-fused indolo[2,3-d]benzazepine-based Schiff bases HL1 and HL2 and metal complexes [M(p-cymene)(HL1)Cl]Cl, where M = Ru (1), Os (2), and [Cu(HL2)Cl2] (3) were synthesized and characterized by spectroscopic techniques (UV–vis, 1H, 13C, 15N–1H HSQC NMR) and ESI mass spectrometry. The molecular structures of B and HL1 were confirmed by single-crystal X-ray diffraction, while that of 3 by electron diffraction of nanometer size crystalline sample. Molecular docking calculations of species B in the binding pocket of PIM-1 enzyme revealed that the 1-amino-hydantoin moiety is not involved in any hydrogen-bonding interactions, even though a good accommodation of the host molecule in the ATP binding pocket of the enzyme was found. The antiproliferative activity of organic compounds B, HL1 and HL2, as well as complexes 1–3 was investigated in lung adenocarcinoma A549, colon adenocarcinoma LS-174 and triple-negative breast adenocarcinoma MDA-MB-231 cells and normal human lung fibroblast cells MRC-5 by MTT assays; then, the results are discussed.

Structural, electronic and nonlinear optical properties, reactivity and solubility of the drug dihydroartemisinin functionalized on the carbon nanotube

Density functional theory (DFT) calculations of the antimalarial drug dihydroartemisinin (DHA) functionalized on the carbon nanotube (CNT) were carried out in gas phase and in solution to investigate the role of fCNTs as a nanovector for the targeted delivery of the DHA drug and to predict their chemical descriptors and electronic and nonlinear optical (NLO) properties. The results of the geometric optimization indicate that the functionalization does not change the molecular structure of DHA. Based on our findings of binding and solvation energies, two energetically stable configurations were identified in 1st (fCNT1-2) and 2nd (2fCNT1-2) functionalization. For these stable configurations, the energy gap value goes from 1.52 eV for the (5,5) single wall pristine CNT to 1.27 eV for the 1st functionalization and to 1.06 eV for the 2nd functionalization regardless of the considered media; which gives these nanostructures excellent semiconductor properties. Findings from global reactivity descriptors show that the reactivity of the functionalized CNT is strongly improved in solvent media and that the stability of DHA decreases while its reactivity increases during the functionalization. Thus, the fundamental gap (E_f) in gas phase decreases from 3.65 eV for the virgin CNT to 3.30 eV for fCNT2 and to 3.02 eV for 2fCNT2. On the contrary, in water E_f goes from 1.20 eV for the virgin CNT to 0.95 eV for fCNT2 and to 0.74 eV for 2fCNT2; demonstrating an improvement in the reactivity of our fCNTs as nanovectors for targeted delivery of DHA drug. Finally, our findings show that these nanostructures may also have good NLO properties and can be promising materials for NLO applications.

Siamenflavones A-C, three undescribed biflavonoids from Selaginella siamensis Hieron. and biflavonoids from spike mosses as EGFR inhibitor

Three undescribed biflavonoids (BFVs), siamenflavones A-C along with twelve BFVs were isolated from Selaginella siamensis Hieron. and Selaginella bryopteris (L.) Baker (Selaginellaceae). The chemical structures of undescribed compounds were established through comprehensive spectroscopic techniques, chemical correlations, and X-ray crystallography. The ten isolated BFVs, siamenflavones A-C, delicaflavone, chrysocauflavone, robustaflavone, robustaflavone-4-methylether, amentoflavone, tetrahydro-amentoflavone, and sciadopitysin were evaluated for the antiproliferative effects against four human cancer cell lines A549, H1975, HepG2 and T47D. Delicaflavone and robustaflavone 4'-methylether exerted strong effects on the four human cancer cell lines. Siamenflavone B, delicaflavone and robustaflavone 4'-methylether showed potent inhibitory activities against wild-type EGFR. The inhibition of the compounds was further supported by molecular docking and predictive intermolecular interactions. Molecular dynamics simulation studies of siamenflavone B and robustaflavone-4'-methylether complexed to EGFR-TK further supported inhibition of the compounds to the ATP binding site. Finally, analysis of pharmacokinetic and electronic properties using density-functional theory and known drug index calculations suggest that the compounds are pharmaceutically compatible for drug administration.

Octanol-Water Partition Coefficients of Fluorinated Drug Molecules with Continuum Solvation Models

In this work, we have examined the efficiency of continuum solvation models, used with density functional theory methods, in calculating octanol-water partition coefficients (logP) of 56 fluorine containing drug molecules. The solvation model based on density model computed logP values that are in good agreement with the benchmark values. The conductor-like polarizable continuum models computed results have issues in predicting correct trend, often with reversal of sign from benchmark. The choice of basis set does not show significant effect, and the selection of atomic radii affects geometry convergence during calculations.

Δ-Quantum machine-learning for medicinal chemistry

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.

Highly Antiproliferative Latonduine and Indolo[2,3-c]quinoline Derivatives: Complex Formation with Copper(II) Markedly Changes the Kinase Inhibitory Profile

A series of latonduine and indoloquinoline derivatives HL¹–HL⁸ and their copper(II) complexes (1–8) were synthesized and comprehensively characterized. The structures of five compounds (HL⁶, [CuCl(L¹)(DMF)]·DMF, [CuCl(L²)(CH₃OH)], [CuCl(L³)]·0.5H₂O, and [CuCl₂(H₂L⁵)]Cl·2DMF) were elucidated by single crystal X-ray diffraction. The copper(II) complexes revealed low micro- to sub-micromolar IC₅₀ values with promising selectivity toward human colon adenocarcinoma multidrug-resistant Colo320 cancer cells as compared to the doxorubicin-sensitive Colo205 cell line. The lead compounds HL⁴ and 4 as well as HL⁸ and 8 induced apoptosis efficiently in Colo320 cells. In addition, the copper(II) complexes had higher affinity to DNA than their metal-free ligands. HL⁸ showed selective inhibition for the PIM-1 enzyme, while 8 revealed strong inhibition of five other enzymes, i.e., SGK-1, PKA, CaMK-1, GSK3β, and MSK1, from a panel of 50 kinases. Furthermore, molecular modeling of the ligands and complexes showed a good fit to the binding pockets of these targets.

Discovery of novel Hsp90 C-terminal domain inhibitors that disrupt co-chaperone binding

Heat shock protein 90 (Hsp90) is an essential molecular chaperone that performs vital stress-related and housekeeping functions in cells and is a current therapeutic target for diseases such as cancers. Particularly, the development of Hsp90 C-terminal domain (CTD) inhibitors is highly desirable as inhibitors that target the N-terminal nucleotide-binding domain may cause unwanted biological effects. Herein, we report on the discovery of two drug-like novel Hsp90 CTD inhibitors by using virtual screening and intrinsic protein fluorescence quenching binding assays, paving the way for future development of new therapies that employ molecular chaperone inhibitors.

Deoxycholic acid as a molecular scaffold for tyrosyl-DNA phosphodiesterase 1 inhibition: A synthesis, structure-activity relationship and molecular modeling study

Para-Bromoanilides of deoxycholic acid with various functional groups on the steroid scaffold were designed as promising tyrosyl-DNA phosphodiesterase 1 (Tdp1) inhibitors. Tdp1 is a DNA repair enzyme, involved in removing DNA damage caused by topoisomerase I poisons; an important class of anticancer drugs. Thus, reducing the activity of Tdp1 can increase the efficacy of anticancer drugs in current use. Inhibitory activity in the low micromolar and submicromolar concentrations was observed with 3,12-dimethoxy para-bromoanilide 17 being the most active with an IC50 value of 0.27 μM. The activity of N-methyl para-bromoanilides was 3-4.8 times lower than of the corresponding para-bromoanilides. Increased potency of the ligands was seen with higher molecular weight and log P values. The ligands were evaluated for their cytotoxic potential in a panel of tumor cell lines; all were nontoxic to the A549 pulmonary adenocarcinoma cell line. However, derivatives containing a hydroxyl group at the 12th position were more toxic than their 12-hydroxyl group counterparts (acetoxy-, oxo- and methoxy- group) against HCT-116 human colon and HepG2 hepatocellular carcinomas. In addition, an N-methyl substitution led to an increase in toxicity for the HCT-116 and HepG2 cell lines. The excellent activity as well as low cytotoxicity, derivative 17 can be considered as a lead compound for further development.

Destabilization of Alzheimer's Aβ42 protofibrils with acyclovir, carmustine, curcumin, and tetracycline: insights from molecular dynamics simulations

Among the neurodegenerative diseases, one of the most common dementia is Alzheimer's disease (AD).

Discovery and Characterisation of Dual Inhibitors of Tryptophan 2,3-Dioxygenase (TDO2) and Indoleamine 2,3-Dioxygenase 1 (IDO1) Using Virtual Screening

Cancers express tryptophan catabolising enzymes indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO2) to produce immunosuppressive tryptophan metabolites that undermine patients’ immune systems, leading to poor disease outcomes. Both enzymes are validated targets for cancer immunotherapy but there is a paucity of potent TDO2 and dual IDO1/TDO2 inhibitors. To identify novel dual IDO1/TDO2 scaffolds, 3D shape similarity and pharmacophore in silico screening was conducted using TDO2 as a model for both systems. The obtained hits were tested in cancer cell lines expressing mainly IDO1 (SKOV3—ovarian), predominantly TDO2 (A172—brain), and both IDO1 and TDO2 (BT549—breast). Three virtual screening hits were confirmed as inhibitors (TD12, TD18 and TD34). Dose response experiments showed that TD34 is the most potent inhibitor capable of blocking both IDO1 and TDO2 activity, with the IC₅₀ value for BT549 at 3.42 µM. This work identified new scaffolds able to inhibit both IDO1 and TDO2, thus enriching the collection of dual IDO1/TDO2 inhibitors and providing chemical matter for potential development into future anticancer drugs.

A Chemometric Analysis of Deep-Sea Natural Products

Deep-sea natural products have been created by unique marine organisms that thrive in a challenging environment of extreme conditions for its inhabitants. In this study, 179 deep-sea natural products isolated from 2009 to 2013 were investigated by analysing their physicochemical properties that are important indicators of the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) profile of a compound. The study and analysis of these molecular descriptors and characteristics enabled the defining of these compounds in various chemical spaces, particularly as an indication of their drug-likeness and position in chemical space and is the first to be conducted to analyse deep-sea derived natural products. It was found that ~40% of all deep-sea natural products were drug-like and 2/3 were within Known Drug Space (KDS), highlighting the high drug-likeness of a significant proportion of deep-sea natural products, most of which have already been shown to have notable biological activities, that should be further investigated as potential therapeutics. Furthermore, this study was able to reveal the general structural differences between compounds from Animalia, Bacteria and Fungi organisms where it was observed that natural products from members of the Animalia kingdom are structurally more varied than compounds from bacteria and fungi. It was also noted that, in general, fungi-derived compounds occupy a more favourable position in drug-like chemical space and are a rich and promising source of biologically-active natural products for the purposes of drug development and therapeutic application.

Chemical Patterns of Proteasome Inhibitors: Lessons Learned from Two Decades of Drug Design

Drug discovery now faces a new challenge, where the availability of experimental data is no longer the limiting step, and instead, making sense of the data has gained a new level of importance, propelled by the extensive incorporation of cheminformatics and bioinformatics methodologies into the drug discovery and development pipeline. These enable, for example, the inference of structure-activity relationships that can be useful in the discovery of new drug candidates. One of the therapeutic applications that could benefit from this type of data mining is proteasome inhibition, given that multiple compounds have been designed and tested for the last 20 years, and this collection of data is yet to be subjected to such type of assessment. This study presents a retrospective overview of two decades of proteasome inhibitors development (680 compounds), in order to gather what could be learned from them and apply this knowledge to any future drug discovery on this subject. Our analysis focused on how different chemical descriptors coupled with statistical tools can be used to extract interesting patterns of activity. Multiple instances of the structure-activity relationship were observed in this dataset, either for isolated molecular descriptors (e.g., molecular refractivity and topological polar surface area) as well as scaffold similarity or chemical space overlap. Building a decision tree allowed the identification of two meaningful decision rules that describe the chemical parameters associated with high activity. Additionally, a characterization of the prevalence of key functional groups gives insight into global patterns followed in drug discovery projects, and highlights some systematically underexplored parts of the chemical space. The various chemical patterns identified provided useful insight that can be applied in future drug discovery projects, and give an overview of what has been done so far.

A Chemometric Analysis of Compounds from Native New Zealand Medicinal Flora

Several hundred (396) compounds from New Zealand flora with medicinal properties were analyzed for their physicochemical properties. It was found that approximately 10 % fulfilled all the requirements to be considered to be lead-like, over half of the compounds were deemed to be in the drug-like space and ≈75 % were in the known drug space. These results indicate the presence of a significant proportion of compounds that are of particular interest to pursue as potential lead compounds or therapeutics. Additionally, compound classes were analyzed separately-most carbonyl-containing compounds (aldehydes, ketones, esters and lactones), along with phenols were the most lead-like compounds, which also displayed very good proportions in the drug-like and known drug space. The information presented herein can be harnessed and utilized in future work, through focussing on the compounds and compound classes that exhibit high-levels of lead-likeness for further development.

Novel tyrosyl-DNA phosphodiesterase 1 inhibitors enhance the therapeutic impact of topoteсan on in vivo tumor models

The druggability of the tyrosyl-DNA phosphodiesterase 1 (Tdp1) enzyme was investigated in conjunction with topoisomerase 1 inhibition. A novel class of thiazole, aminothiazole and hydrazonothiazole usnic acid derivatives was synthesized and evaluated as Tdp1 inhibitors and their ability to sensitize tumors to topotecan, a topoisomerase inhibitor in clinical use. Of all the compounds tested, four hydrazinothiazole derivatives, 20c, 20d, 20h and 20i, inhibited the enzyme in the nanomolar range. The activity of the compounds was verified by affinity experiments as well as supported by molecular modelling. The most effective Tdp1 inhibitor, 20d, was ton-toxic and increased the effect of topotecan both in vitro and in vivo in the Lewis lung carcinoma model. Furthermore, 20d showed significant increase in the antitumor and antimetastatic effect of topotecan in mice. The results presented here justify compound 20d to be considered as a drug lead for antitumor therapy.

The Development of a Weighted Index to Optimise Compound Libraries for High Throughput Screening

1880 known drugs were collected and analysed for their mainstream molecular descriptors: MW, log P, HA, HD, RB and PSA. The statistical distributions were fitted to Gaussian functions for each of the descriptors. This gave a mathematical tool to calculate a weighted score, or an Index, for each descriptor. Known Drug Indexes (KDIs) were derived either by summation or multiplication of the Indexes, giving one number for each molecule calculated. The KDI summation and multiplication methods give a theoretical maxima of 6 and 1 respectively. According to both methods, methysergide (5.89/0.90), amsacrine (5.89/0.89) and fluorometholone (5.88/0.88) have the scores of the most well-balanced pharmaceuticals. The KDIs are advantageous tools in identifying the most well-balanced screening compounds based on the properties of known drugs; the screening collection can be optimised to only include quality compounds, which in turn produce tractable hit and lead compounds from the screening campaign.

Machine learning for the prediction of molecular dipole moments obtained by density functional theory

Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML schemes. A database was used with 10,071 structures, new molecular descriptors were designed and the models were validated with external test sets. Several ML algorithms were screened. Random forest regression models predicted an external test set of 3368 compounds achieving mean absolute error up to 0.44 D. The results represent a significant improvement of the dipole moments calculated using empirical point charges located at the nucleus, even assuming the DFT-optimized geometry (root mean square error, RMSE, of 0.68 D vs. 1.53 D and R² = 0.87 vs. 0.66).

Lignans: A Chemometric Analysis

The physicochemical properties of classical lignans, neolignans, flavonolignans and carbohydrate-lignan conjugates (CLCs) were analysed to assess their ADMET profiles and establish if these compounds are lead-like/drug-like and thus have potential to be or act as leads in the development of future therapeutics. It was found that while no studied compounds were lead-like, a very large proportion (>75%) fulfilled all the requirements to be deemed as present in drug-like space and almost all compounds studied were in the known drug space. Principal component analysis was an effective technique that enabled the investigation of the relationship between the studied molecular descriptors and was able to separate the lignans from their sugar derivatives and flavonolignans, primarily according to the parameters that are considered when defining chemical space (i.e., number of hydrogen bond donors, acceptors, rotatable bonds, polar surface area and molecular weight). These results indicate that while CLCs and flavonolignans are less drug-like, lignans show a particularly high level of drug-likeness, an observation that coupled with their potent biological activities, demands future pursuit into their potential for use as therapeutics.