The Art and Science of Molecular Docking.

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

A synthetic agent ameliorates polycystic kidney disease by promoting apoptosis of cystic cells through increased oxidative stress.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of chronic kidney disease and the fourth leading cause of end-stage kidney disease, accounting for over 50% of prevalent cases requiring renal replacement therapy. There is a pressing need for improved therapy for ADPKD. Recent insights into the pathophysiology of ADPKD revealed that cyst cells undergo metabolic changes that up-regulate aerobic glycolysis in lieu of mitochondrial respiration for energy production, a process that ostensibly fuels their increased proliferation. The present work leverages this metabolic disruption as a way to selectively target cyst cells for apoptosis. This small-molecule therapeutic strategy utilizes 11beta-dichloro, a repurposed DNA-damaging anti-tumor agent that induces apoptosis by exacerbating mitochondrial oxidative stress. Here, we demonstrate that 11beta-dichloro is effective in delaying cyst growth and its associated inflammatory and fibrotic events, thus preserving kidney function in perinatal and adult mouse models of ADPKD. In both models, the cyst cells with homozygous inactivation of Pkd1 show enhanced oxidative stress following treatment with 11beta-dichloro and undergo apoptosis. Co-administration of the antioxidant vitamin E negated the therapeutic benefit of 11beta-dichloro in vivo, supporting the conclusion that oxidative stress is a key component of the mechanism of action. As a preclinical development primer, we also synthesized and tested an 11beta-dichloro derivative that cannot directly alkylate DNA, while retaining pro-oxidant features. This derivative nonetheless maintains excellent anti-cystic properties in vivo and emerges as the lead candidate for development.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects.

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

Synthesis and biological evaluation of orally active anti-Trypanosoma agents.

In previous studies, we developed anti-trypanosome tubulin inhibitors with promising in vitro selectivity and activity against Human African Trypanosomiasis (HAT). However, for such agents, oral activity is crucial. This study focused on further optimizing these compounds to enhance their ligand efficiency, aiming to reduce bulkiness and hydrophobicity, which should improve solubility and, consequently, oral bioavailability. Using Trypanosoma brucei brucei cells as the parasite model and human normal kidney cells and mouse macrophage cells as the host model, we evaluated 30 new analogs synthesized through combinatorial chemistry. These analogs have fewer aromatic moieties and lower molecular weights than their predecessors. Several new analogs demonstrated IC50s in the low micromolar range, effectively inhibiting trypanosome cell growth without harming mammalian cells at the same concentration. We conducted a detailed structure-activity relationship (SAR) analysis and a docking study to assess the compounds' binding affinity to trypanosome tubulin homolog. The results revealed a correlation between binding energy and anti-Trypanosoma activity. Importantly, compound 7 displayed significant oral activity, effectively inhibiting trypanosome cell proliferation in mice.

Optimization of benzenesulfonyl derivatives as anti-Trypanosomatidae agents: Structural design, synthesis, and pharmacological assessment against Trypanosoma cruzi and Leishmania infantum.

Leishmaniasis and Chagas disease are neglected tropical diseases caused by Trypanosomatidae parasites. Given the numerous limitations associated with current treatments, such as extended treatment duration, variable efficacy, and severe side effects, there is an urgent imperative to explore novel therapeutic options. This study details the early stages of hit-to-lead optimization for a benzenesulfonyl derivative, denoted as initial hit, against Trypanossoma cruzi (T. cruzi), Leishmania infantum (L. infantum) and Leishmania braziliensis (L. braziliensis). We investigated structure - activity relationships using a series of 26 newly designed derivatives, ultimately yielding potential lead candidates with potent low-micromolar and sub-micromolar activities against T. cruzi and Leishmania spp, respectively, and low in vitro cytotoxicity against mammalian cells. These discoveries emphasize the significant promise of this chemical class in the fight against Chagas disease and leishmaniasis.

Bioisosteric heterocyclic analogues of natural bioactive flavonoids by scaffold-hopping approaches: State-of-the-art and perspectives in medicinal chemistry.

The flavonoid family is a set of well-known bioactive natural molecules, with a wide range of potential therapeutic applications. Despite the promising results obtained in preliminary in vitro/vivo studies, their pharmacokinetic and pharmacodynamic profiles are severely compromised by chemical instability. To address this issue, the scaffold-hopping approach is a promising strategy for the structural optimization of natural leads to discover more potent analogues. In this scenario, this Perspective provides a critical analysis on how the replacement of the chromon-4-one flavonoid core with other bioisosteric nitrogen/sulphur heterocycles might affect the chemical, pharmaceutical and biological properties of the resulting new chemical entities. The investigated derivatives were classified on the basis of their biological activity and potential therapeutic indications. For each session, the target(s), the specific mechanism of action, if available, and the key pharmacophoric moieties were highlighted, as revealed by X-ray crystal structures and in silico structure-based studies. Biological activity data, in vitro/vivo studies, were examined: a particular focus was given on the improvements observed with the new heterocyclic analogues compared to the natural flavonoids. This overview of the scaffold-hopping advantages in flavonoid compounds is of great interest to the medicinal chemistry community to better exploit the vast potential of these natural molecules and to identify new bioactive molecules.

MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design.

MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

Development and Characterization of Novel Selective, Non-Basic Dopamine D2 Receptor Antagonists for the Treatment of Schizophrenia.

The dopamine D2 receptor, which belongs to the family of G protein-coupled receptors (GPCR), is an important and well-validated drug target in the field of medicinal chemistry due to its wide distribution, particularly in the central nervous system, and involvement in the pathomechanism of many disorders thereof. Schizophrenia is one of the most frequent diseases associated with disorders in dopaminergic neurotransmission, and in which the D2 receptor is the main target for the drugs used. In this work, we aimed at discovering new selective D2 receptor antagonists with potential antipsychotic activity. Twenty-three compounds were synthesized, based on the scaffold represented by the D2AAK2 compound, which was discovered by our group. This compound is an interesting example of a D2 receptor ligand because of its non-classical binding to this target. Radioligand binding assays and SAR analysis indicated structural modifications of D2AAK2 that are possible to maintain its activity. These findings were further rationalized using molecular modeling. Three active derivatives were identified as D2 receptor antagonists in cAMP signaling assays, and the selected most active compound 17 was subjected to X-ray studies to investigate its stable conformation in the solid state. Finally, effects of 17 assessed in animal models confirmed its antipsychotic activity in vivo.

Molecular Modelling Study and Antibacterial Evaluation of Diphenylmethane Derivatives as Potential FabI Inhibitors.

The need for new antibiotics has become a major worldwide challenge as bacterial strains keep developing resistance to the existing drugs at an alarming rate. Enoyl-acyl carrier protein reductases (FabI) play a crucial role in lipids and fatty acid biosynthesis, which are essential for the integrity of the bacterial cell membrane. Our study aimed to discover small FabI inhibitors in continuation to our previously found hit MN02. The process was initially started by conducting a similarity search to the NCI ligand database using MN02 as a query. Accordingly, ten compounds were chosen for the computational assessment and antimicrobial testing. Most of the compounds showed an antibacterial activity against Gram-positive strains, while RK10 exhibited broad-spectrum activity against both Gram-positive and Gram-negative bacteria. All tested compounds were then docked into the saFabI active site followed by 100 ns MD simulations (Molecular Dynamics) and MM-GBSA (Molecular Mechanics with Generalised Born and Surface Area Solvation) calculations in order to understand their fitting and estimate their binding energies. Interestingly, and in line with the experimental data, RK10 was able to exhibit the best fitting with the target catalytic pocket. To sum up, RK10 is a small compound with leadlike characteristics that can indeed act as a promising candidate for the future development of broad-spectrum antibacterial agents.

Deep generative models for ligand-based de novo design applied to multi-parametric optimization.

Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AI-designed compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one. The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.

Lead optimization of cathepsin K inhibitors for the treatment of Osteoarthritis.

Cathepsin K (Cat K) is a cysteine protease involved in bone remodeling. In addition to its role in bone biology, Cat K is upregulated in osteoclasts, chondrocytes and synoviocytes in osteoarthritic (OA) disease states making it a potential therapeutic target for disease-modifying OA. Starting from a prior preclinical compound, MK-1256, lead optimization efforts were carried out in the search for potent Cat K inhibitors with improved selectivity profiles with an emphasis on cathepsin F. Herein, we report the SAR studies which led to the discovery of the highly selective oxazole compound 23, which was subsequently shown to inhibit cathepsin K in vivo as measured by reduced levels of urinary C-telopeptide of collagen type I in dog.

A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.

We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

Synthesis and biological evaluation of imidamide analogs as selective anti-trypanosomal agents.

Human African trypanosomiasis is caused by a protozoan parasite Trypanosoma brucei majorly infecting people living in sub-Saharan Africa. Current limited available treatments suffer from drug resistance, severe adverse effects, low efficacy, and costly administrative procedures in African countries with limited medical resources. Therefore, there is always a perpetual demand for advanced drug development and invention of new strategies to combat the disease. Previous work in our lab generated a library of sulfonamide analogs as selective tubulin inhibitors, based on the structural difference between mammalian and trypanosome tubulin proteins. Further lead derivatization was performed in the current study and generated 25 potential drug candidates to improve the drug efficacy and uptake by selectively targeting the parasite's P2 membrane transporter protein with imidamide moiety. One of the newly synthesized analogs, compound 25 with a di-imidamide moiety, has shown greater potency with an IC50 of 1 nM to selectively inhibit the growth of trypanosome cells without affecting the viability of mammalian cells. Western blot analyses reveal that the compound suppressed tubulin polymerization in T. brucei cells. A detailed structure-activity relationship (SAR) was summarized that will be used to guide future lead optimization.

Discovery and optimization of orally bioavailable and potent plasma Kallikrein inhibitors bearing a quaternary carbon.

Hereditary angioedema (HAE) is a rare and potentially life-threatening disease that affects an estimated 1 in 50,000 individuals worldwide. Berotralstat (BCX7353) is the only small molecule approved by the US Food and Drug Administration (FDA) for the prophylactic treatment of HAE attacks in patients 12 years and older. During the discovery of BCX7353, we also identified a novel series of small molecules containing a quaternary carbon as potent and orally bioavailable Plasma Kallikrein (PKal) inhibitors. Lead compound was identified as a potent inhibitor following a detailed lead optimization process that balanced the lipophilic efficiency (LipE) and pharmacokinetic (PK) profile.

Lead optimization of pyrido[2,3-d][1]benzazepin-6-one derivatives leading to the discovery of a potent, selective, and orally available human parathyroid hormone receptor 1 (hPTHR1) antagonist (DS69910557).

We report the discovery of a series of novel zwitterionic hPTHR1 antagonists. Optimization of lead compound 2 led to 4-[[1-[4-(2,9-dichloro-5,5-dimethyl-6-oxo-pyrido[2,3-d][1]benzazepin-7-yl)phenyl]-3-fluoro-azetidin-3-yl]methylamino]cyclohexanecarboxylic acid (19e, DS69910557), a compound with excellent potency and selectivity over activity at the human ether-a-go-go-related-gene (hERG) channel. Compound 19e demonstrated in vivo potency to decrease the plasma calcium concentration in rats upon oral administration. 2022 Elsevier Ltd. All rights reserved.

Gallic acid derivatives as inhibitors of mussel (Mytilus galloprovincialis) larval settlement: Lead optimization, biological evaluation and use in antifouling coatings.

The addition of biocides to marine coatings has been the most used solution to avoid marine biofouling, however they are persistent, bioaccumulative, and toxic (PBT) to marine ecosystems. The development of natural products or Nature-inspired synthetic compounds to replace these harmfull biocides has been pursued as one of the most promising antifouling (AF) alternatives. Following a bioprospection strategy, we have previously reported the AF activity of gallic acid persulfate (1) against the settlement of Mytilus galloprovincialis larvae (EC50 = 18 µM and LC50/EC50 = 27) without exhibiting ecotoxicity to Artemia salina. In this work, a lead optimization strategy was applied to compound 1 in order to improve potency while maintaining a low ecotoxicity profile. In this direction, twenty-seven compounds were synthesized, from which eighteen were obtained for the first time. An AF screening was performed against the settlement of mussel M. galloprovincialis larvae and derivative 26, 2-(3,4,5-trihydroxybenzamido)ethan-1-aminium bromide, was found to be more potent (EC50 = 3 µM and LC50/EC50 = 73) than compound 1 and the biocide Econea® (EC50 = 4 µM). The potential impact on neurotransmission, and ecotoxicity against two non-target marine organisms was also evaluated. Marine polyurethane (PU)-based coatings containing compound 26 were prepared and lower adherence of mussel larvae was observed compared to compound 26 free PU-coatings. Studies concerning the leaching of compound 26 from the prepared coating were also conducted, and < 10% of this compound was detected after 45 days of submersion in water. Overall, we have optimized the potency against the settlement of mussels of our initial lead compound, not compromising the toxicity and compatibility with PU-based coatings.

Lead optimization and biological evaluation of diazenylbenzenesulfonamides inhibitors against glyoxalase-I enzyme as potential anticancer agents.

In a previous report, we described the discovery of (E)-5-((8-hydroxyquinolin-5-yl)diazenyl)-2-methylbenzenesulfonamide as a potent inhibitor of GLO-I enzyme with IC50 of 1.28 ± 0.12 µM. Herein, lead optimization of this compound was achieved through targeting the central zinc atom and hydrophilic amino acid residues in the active site of the enzyme. Among the synthesized compounds, compound TS010 showed the most potent inhibitory activity with IC50 of 0.57 ± 0.04 µM. Compound TS013 also showed comparable activity to that of the lead compound with IC50 of 1.14 ± 0.03 µM. Molecular docking studies disclosed the binding mode of the compounds inside the active side of GLO-I enzyme.