Bioactive conformational generation of small molecules: a comparative analysis between force-field and multiple empirical criteria based methods

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.

Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory

We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.

Herbacetin Broadly Blocks the Activities of CYP450s by Different Inhibitory Mechanisms

Herbacetin is a bioactive flavanol compound that has various pharmacological effects. However, the pharmacokinetic characteristics have not been thoroughly investigated. Previously, we screened a natural compound library and identified herbacetin as a potent CYP blocker. Herein, we aimed to mechanistically determine the inhibitory effects of herbacetin on CYP450 and its potential application. A human liver microsome incubation system was developed based on a UPLC-MS/MS method. Moreover, an in silico docking assay and a human CYP recombinase reaction system were developed and used to investigate binding affinity and inhibitory efficacy. Subsequently, the effects of the combination of herbacetin and sorafenib on HepG2 cells were assessed by MTT and immunoblotting assays. The concentration of sorafenib and its main metabolite were measured by UPLC-MS/MS after incubation with or without herbacetin. As a result, we found herbacetin almost completely inhibited the functions of major CYPs at 100 µM. Moreover, through analysis of the structure-activity relationship, we found 4-, 6-, and 8-hydroxyl were essential groups for the inhibitory effects. Herbacetin inhibited CYP3A4, CYP2B6, CYP2C9, and CYP2E1 in a mixed manner, but non-competitively blocked CYP2D6. These results are in good agreement with the recombinase reaction in vitro results, with an IC₅₀ < 10 µM for each tested isoenzyme. Interestingly, the stimulatory effects of sorafenib on HepG2 cell apoptosis were significantly enhanced by combining with herbacetin, which was associated with increased sorafenib exposure. In summary, herbacetin is a potent inhibitor of a wide spectrum of CYP450s, which may enhance the exposure of drugs in vivo.

Comparing the Performances of Force Fields in Conformational Searching of Hydrogen-Bond-Donating Catalysts

Here, we compare the relative performances of different force fields for conformational searching of hydrogen-bond-donating catalyst-like molecules. We assess the force fields by their predictions of conformer energies, geometries, low-energy, nonredundant conformers, and the maximum numbers of possible conformers. Overall, MM3, MMFFs, and OPLS3e had consistently strong performances and are recommended for conformationally searching molecules structurally similar to those in this study.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to spread globally with more than 172 million confirmed cases and 3.57 million deaths. Cyclic sulfonamide derivative is identified as a successful compound and showed anti-SARS-CoV-2 activity. In this study, the structure and activity relationships of 35 cyclic sulfonamide compound inhibitors are investigated by using three-dimensional quantitative structure-activity relationship (3D-QSAR) and holographic quantitative structure-activity relationship (HQSAR). Two models with good statistical parameters and reliable predictive ability are obtained from the same training set, including Topomer CoMFA (q2= 0.623,r2= 0.938,rpred2= 0.893) model and HQSAR (q2= 0.704,r2= 0.958,rpred2=0.779) model. The established models not only have good stability, but also show good external prediction ability for the test set. The contour and color code maps of the models provide a lot of useful information for determining the structural requirements which might affect the activity; this information paves the way for the design of four novel cyclic sulfonamide compounds, and predictes their pIC₅₀ values. We explore the interaction between the newly designed molecule and SARS-CoV-2 3CLpro by molecular docking. The docking results show that GLU166, GLN192, ALA194, and VAL186 may be the potential active residues of the SARS-CoV-2 inhibitor evaluated in this study. Finally, the oral bioavailability and toxicity of the newly designed cyclic sulfonamide compounds are evaluated and the results show that the four newly designed cyclic sulfonamide compounds have major ADMET properties and can be used as reliable inhibitors against COVID-19. These results may provide useful insights for the design of effective SARS-CoV-2 inhibitors.

Benchmark of Generic Shapes for Macrocycles

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional “basic knowledge” alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Accuracy evaluation and addition of improved dihedral parameters for the MMFF94s

The Platinum dataset of protein-bound ligand conformations was used to benchmark the ability of the MMFF94s force field to generate bioactive conformations by minimization of randomly generated conformers. Torsion angle parameters that generally caused wrong geometries were reparameterized by conducting dihedral scans using ab initio calculations at the MP2 level. This reparameterization resulted in a systematic improvement of generated conformations.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Conformation Generation: The State of the Art

The generation of conformations for small molecules is a problem of continuing interest in cheminformatics and computational drug discovery. This review will present an overview of methods used to sample conformational space, focusing on those methods designed for organic molecules commonly of interest in drug discovery. Different approaches to both the sampling of conformational space and the scoring of conformational stability will be compared and contrasted, with an emphasis on those methods suitable for conformer sampling of large numbers of drug-like molecules. Particular attention will be devoted to the appropriate utilization of information from experimental solid-state structures in validating and evaluating the performance of these tools. The review will conclude with some areas worthy of further investigation.

Searching for bioactive conformations of drug-like ligands with current force fields: how good are we?

Drug-like ligands obtained from protein-ligand complexes deposited in the Protein Databank were subjected to conformational searching using various force fields and solvation settings. For each ligand, the resulting conformer pool was examined for the presence of the bioactive (crystal pose-like) conformation. Similarity of conformers toward the crystal-pose was quantified as the best achievable root mean squared deviation (RMSD, heavy atoms only). Analyzing the conformer pools generated by various force fields revealed only small differences in the likelihood of finding a crystal pose-like conformation. However, employing different solvents in the conformational search was found to be very important for achieving RMSDs below 1.0 Å. The best statistical values of likelihood were observed with a recently released force field covering a large portion of dihedral angles occurring in drug-like compounds in combination with the water as solvent. In order to enable computational chemists and modelers to efficiently use available software tools, we have additionally performed several focused analyses on ligands, grouped according to descriptors most relevant for the rational drug design.

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .

Physicochemical analyses of a bioactive 4-aminoantipyrine analogue - synthesis, crystal structure, solid state interactions, antibacterial, conformational and docking studies

A novel Schiff base derivative of 4-aminoantipyrine, that is, (E)-4-(2-methoxybenzylideneamino)-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one (MBA-dMPP), was synthesized and characterized by FT-IR, 1H-NMR, and EI-MS. Single-crystal X-ray diffraction data revealed MBA-dMPP adopts a trans configuration around its central C=N double bond, and forms orthorhombic crystals. XRD revealed that MBA-dMPP possess two different planes, in which the pyrazolone and benzylidene groups attached to C9 of the pyrazolone ring are almost coplanar and the phenyl ring connected to the N1 atom of the pyrazolone moiety lies in another plane. The intermolecular, host-guest C-H…O, C-H…N, and C-H…C van der Waals interactions were found to form a 3D network and confer stability to the MBA-dMPP crystal structure. The quantitative and qualitative solid state behaviors of MBA-dMPP were subjected to 3D Hirshfeld surface analysis and 2D fingerprint plotting. Reciprocal H…H contacts contributed most (52.9 %) to the Hirshfeld surface, followed by C…H/H…C contacts (30.2 %), whereas, O…H/H…O and N…H/H…N interactions contributed 15.5 % to the Hirshfeld surface. Electrostatic potentials were mapped over the Hirshfeld surface to analyze electrostatic complementarities within the MBA-dMPP crystal. In addition, geometrical descriptors were also analyzed to the extent of surface interactions. MBA-dMPP was also investigated for in vitro antibacterial activity against Gram-positive and Gram-negative bacterial strains, and showed highest activity against Bacillus cereus (MIC = 12.5 μg mL-1) and Salmonellatythimurium (MIC = 50 μg mL-1). In silico screening was conducted by docking MBA-dMPP on the active site of S12 bacterial protein (an important therapeutic target of antibacterial agents) and its binding properties were compared with those of ciprofloxacin. Moreover, a field points map of MBA-dMPP ligand was studied to determine electrostatic and van der Waals forces, hydrophobic potentials, and positions involved in ligand-receptor interactions. Finally, the torsion energies of crystal structure and optimized and bioactive conformers of MBA-dMPP were compared to predict its bioactive conformation.

iDrug: a web-accessible and interactive drug discovery and design platform

BACKGROUND

The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed.

RESULTS

We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work.

CONCLUSIONS

iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.

Computational drug discovery

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.