ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation.

Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus of in silico strategies is still put on the provision of the desired activity of a compound to the considered target, characterization of a compound in terms of its physicochemical and ADMET properties becomes an indispensable element of computer-aided drug design protocols. In the study, an online application ADMET-PrInt for in silico assessment of selected compound features: cardiotoxicity, solubility, genotoxicity, membrane permeability, and plasma protein binding was prepared. In addition to the prediction of particular property, ADMET-PrInt enables also the identification of compound features influencing this property thanks to the application of two explainability approaches: local interpretabile model-agnostic explanations and counterfactual analysis. It is an important factor for medicinal chemists, as it greatly facilitates the process of optimization of the compound structure in terms of the evaluated properties. The intuitive webpage, available at admet.if-pan.krakow.pl, allows making use of all predictive and interpretability models also by nonexperts and nonprogrammers.

First-in-Class Selenium-Containing Potent Serotonin Receptor 5-HT6 Agents with a Beneficial Neuroprotective Profile against Alzheimer's Disease.

Alzheimer's disease (AD) has a complex and not-fully-understood etiology. Recently, the serotonin receptor 5-HT6 emerged as a promising target for AD treatment; thus, here a new series of 5-HT6R ligands with a 1,3,5-triazine core and selenoether linkers was explored. Among them, the 2-naphthyl derivatives exhibited strong 5-HT6R affinity and selectivity over 5-HT1AR (13-15), 5-HT7R (14 and 15), and 5-HT2AR (13). Compound 15 displayed high selectivity for 5-HT6R over other central nervous system receptors and exhibited low risk of cardio-, hepato-, and nephrotoxicity and no mutagenicity, indicating its "drug-like" potential. Compound 15 also demonstrated neuroprotection against rotenone-induced neurotoxicity as well as antioxidant and glutathione peroxidase (GPx)-like activity and regulated antioxidant and pro-inflammatory genes and NRF2 nuclear translocation. In rats, 15 showed satisfying pharmacokinetics, penetrated the blood-brain barrier, reversed MK-801-induced memory impairment, and exhibited anxiolytic-like properties. 15's neuroprotective and procognitive-like effects, stronger than those of the approved drug donepezil, may pave the way for the use of selenotriazines to inhibit both causes and symptoms in AD therapy.

Structure-Activity Relationships and Therapeutic Potential of Purinergic P2X7 Receptor Antagonists.

The purinergic P2X7 receptor (P2X7R), an ATP-gated non-selective cation channel, has emerged as a gatekeeper of inflammation that controls the release of proinflammatory cytokines. As a key player in initiating the inflammatory signaling cascade, the P2X7 receptor is currently under intense scrutiny as a target for the treatment of different pathologies, including chronic inflammatory disorders (rheumatoid arthritis and osteoarthritis), chronic neuropathic pain, mood disorders (depression and anxiety), neurodegenerative diseases, ischemia, cancer (leukemia), and many others. For these reasons, pharmaceutical companies have invested in discovering compounds able to modulate the P2X7R and filed many patent applications. This review article presents an account of P2X7R structure, function, and tissue distribution, emphasizing its role in inflammation. Next, we illustrate the different chemical classes of non-competitive P2X7R antagonists reported by highlighting their properties and qualities as clinical candidates for treating inflammatory disorders and neurodegenerative diseases. We also discuss the efforts to develop effective Positron Emission Tomography (PET) radioligands to progress the understanding of the pathomechanisms of neurodegenerative disorders, to provide evidence of drug-target engagement, and to assist clinical dose selection for novel drug therapies.

The Subtype Selectivity in Search of Potent Hypotensive Agents among 5,5-Dimethylhydantoin Derived α1-Adrenoceptors Antagonists.

In order to find new hypotensive drugs possessing higher activity and better selectivity, a new series of fifteen 5,5-dimethylhydantoin derivatives (1-15) was designed. Three-step syntheses, consisting of N-alkylations using standard procedures as well as microwaves, were carried out. Crystal structures were determined for compounds 7-9. All of the synthesized 5,5-dimethylhydantoins were tested for their affinity to α1-adrenergic receptors (α1-AR) using both in vitro and in silico methods. Most of them displayed higher affinity (Ki < 127.9 nM) to α1-adrenoceptor than urapidil in radioligand binding assay. Docking to two subtypes of adrenergic receptors, α1A and α1B, was conducted. Selected compounds were tested for their activity towards two α1-AR subtypes. All of them showed intrinsic antagonistic activity. Moreover, for two compounds (1 and 5), which possess o-methoxyphenylpiperazine fragments, strong activity (IC50 < 100 nM) was observed. Some representatives (3 and 5), which contain alkyl linker, proved selectivity towards α1A-AR, while two compounds with 2-hydroxypropyl linker (11 and 13) to α1B-AR. Finally, hypotensive activity was examined in rats. The most active compound (5) proved not only a lower effective dose than urapidil but also a stronger effect than prazosin.

Chalcogen-Varied Imidazolone Derivatives as Antibiotic Resistance Breakers in Staphylococcus aureus Strains.

In this study, a search for new therapeutic agents that may improve the antibacterial activity of conventional antibiotics and help to successfully overcome methicillin-resistant Staphylococcus aureus (MRSA) infections has been conducted. The purpose of this work was to extend the scope of our preliminary studies and to evaluate the adjuvant potency of new derivatives in a set of S. aureus clinical isolates. The study confirmed the high efficacy of piperazine derivatives of 5-arylideneimidazol-4-one (7-9) tested previously, and it enabled the authors to identify even more efficient modulators of bacterial resistance among new analogs. The greatest capacity to enhance oxacillin activity was determined for 1-benzhydrylpiperazine 5-spirofluorenehydantoin derivative (13) which, at concentrations as low as 0.0625 mM, restores the effectiveness of ß-lactam antibiotics against MRSA strains. In silico studies showed that the probable mechanism of action of 13 is related to the binding of the molecule with the allosteric site of PBP2a. Interestingly, thiazole derivatives tested were shown to act as both oxacillin and erythromycin conjugators in S. aureus isolates, suggesting a complex mode of action (i.e., influence on the Msr(A) efflux pump). This high enhancer activity indicates the high potential of imidazolones to become commercially available antibiotic adjuvants.

Extended study on atomic featurization in graph neural networks for molecular property prediction.

Graph neural networks have recently become a standard method for analyzing chemical compounds. In the field of molecular property prediction, the emphasis is now on designing new model architectures, and the importance of atom featurization is oftentimes belittled. When contrasting two graph neural networks, the use of different representations possibly leads to incorrect attribution of the results solely to the network architecture. To better understand this issue, we compare multiple atom representations by evaluating them on the prediction of free energy, solubility, and metabolic stability using graph convolutional networks. We discover that the choice of atom representation has a significant impact on model performance and that the optimal subset of features is task-specific. Additional experiments involving more sophisticated architectures, including graph transformers, support these findings. Moreover, we demonstrate that some commonly used atom features, such as the number of neighbors or the number of hydrogens, can be easily predicted using only information about bonds and atom type, yet their explicit inclusion in the representation has a positive impact on model performance. Finally, we explain the predictions of the best-performing models to better understand how they utilize the available atomic features.

Hydrophobicity modulation via the substituents at positions 2 and 4 of 1,3,5-triazine to enhance therapeutic ability against Alzheimer's disease for potent serotonin 5-HT6R agents.

Alzheimer's disease (AD), a neurodegenerative disorder with a complex aetiology, is the most common memory dysfunction particularly affecting the elderly. Various protein targets have been classified to be involved in the AD treatment, including 5-HT6 receptor (5-HT6R). So far, the 5-HT6R ligands obtained by our research group have become a good basis for hydrophobicity modulation to give a chance for more effective action toward AD by additional influence on target enzymes, e.g. cyclin-dependent kinase 5 (CDK5). In the search for 5-HT6R agents with additional inhibitory action on the enzyme, a series of 25 new 1,3,5-triazines (7-31) as modifications of lead, 4-[1-(2,5-dichlorophenoxy)propyl]-6-(4-methylpiperazin-1-yl)-1,3,5-triazine-2-amine (6), was rationally designed. Molecular modelling, synthesis, crystallographic studies, in vitro biological assays and behavioral studies in vivo were performed. The new triazines showed high affinity (Ki < 100 nM) and selectivity for 5-HT6R. The most effective one, 4-[1-(2,5-difluorophenoxy)propyl]-6-(4-methylpiperazin-1-yl)-1,3,5-triazine-2-amine (8), exhibited the strong antagonistic action towards 5-HT6R (Ki = 5 nM, pKb = 8.16), had an impact on the memory processes in the Novel Object Recognition test and displayed anxiolytic-like activity in the Elevated Plus Maze test in rats. Moreover, it had the antiplatelet effect as well as very good permeability (PAMPA model), high metabolic stability (RLMs) and satisfactory safety in vitro. Although the CDK5 inhibitory effects in vitro for the tested compounds (8, 10, 14, 18, 26-31) missed the potency expected from in silico simulations, the novel antagonist (8) with a very satisfying pharmacological and ADMET profile can serve as a new lead structure in further searches for innovative therapy against AD with accompanying symptoms.

Dual Piperidine-Based Histamine H3 and Sigma-1 Receptor Ligands in the Treatment of Nociceptive and Neuropathic Pain.

In search of new dual-acting histamine H3/sigma-1 receptor ligands, we designed a series of compounds structurally based on highly active in vivo ligands previously studied and described by our team. However, we kept in mind that within the previous series, a pair of closely related compounds, KSK67 and KSK68, differing only in the piperazine/piperidine moiety in the structural core showed a significantly different affinity at sigma-1 receptors (σ1Rs). Therefore, we first focused on an in-depth analysis of the protonation states of piperazine and piperidine derivatives in the studied compounds. In a series of 16 new ligands, mainly based on the piperidine core, we selected three lead structures (3, 7, and 12) for further biological evaluation. Compound 12 showed a broad spectrum of analgesic activity in both nociceptive and neuropathic pain models based on the novel molecular mechanism.

Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark.

Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a widely used computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that various graph-based generative models fail to propose molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we also include simpler tasks in the benchmark based on a simpler scoring function. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone toward the goal of automatically generating promising drug candidates.

Docking-based generative approaches in the search for new drug candidates.

Despite the popularity of virtual screening (VS) of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design (SBDD). In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design (CADD). In addition, we discuss the most promising directions for the further development of generative protocols coupled with docking.

Prediction of probability distributions of molecular properties: towards more efficient virtual screening and better understanding of compound representations.

Various in silico approaches to predict activity and properties of chemical compounds constitute nowadays the basis of computer-aided drug design. While there is a general focus on the predictions of values, mathematically more appropriate is the prognosis of probability distributions, which offers additional possibilities, such as the evaluation of uncertainty, higher moments, and quantiles. In this study, we applied the Hierarchical Correlation Reconstruction approach to assess several ADMET properties of chemical compounds. It uses multiple linear regression to independently assess multiple moments, which are then finally combined into predicted probability distribution. The method enables inexpensive selection of compounds with properties nearly certain to fall into the particular range during virtual screening and automatic rejection of predictions characterized by high rate of uncertainty; however, unlike to the currently used virtual screening methods, it focuses on the prediction of the property distribution, not its actual value. Moreover, the presented protocol enables detection of structural features, which should be carefully considered when optimizing compounds towards particular property, as well as it provides deeper understanding of the examined compound representations.

Generation of new inhibitors of selected cytochrome P450 subtypes- In silico study.

Physicochemical and pharmacokinetic compound profile has crucial impact on compound potency to become a future drug. Ligands with desired activity profile cannot be used for treatment if they are characterized by unfavourable physicochemical or ADMET properties. In the study, we consider metabolic stability and focus on selected subtypes of cytochrome P450 - proteins, which take part in the first phase of compound transformations in the organism. We develop a protocol for generation of new potential inhibitors of selected cytochrome isoforms. Its subsequent stages are composed of generation and assessment of new derivatives of known cytochrome inhibitors, docking and evaluation of the compound possible inhibition on the basis of the obtained ligand-protein complexes. Besides the library of new potential agents inhibiting particular cytochrome subtypes, we also prepare a graph neural network that predicts the change in activity for all modifications of the starting molecule. In addition, we perform a systematic statistical study on the influence of particular substitutions on the potential inhibition properties of generated compounds (both mono- and di-substitutions are considered), provide explanations of the inhibitory predictions and prepare an on-line visualization platform enabling manual inspection of the results. The developed methodology can greatly support the design of new cytochrome P450 inhibitors with the overarching goal of generation of new metabolically stable compounds. It enables instant evaluation of possible compound-cytochrome interactions and selection of ligands with the highest potential of possessing desired biological activity.

Multitargeting the Action of 5-HT6 Serotonin Receptor Ligands by Additional Modulation of Kinases in the Search for a New Therapy for Alzheimer's Disease: Can It Work from a Molecular Point of View?

In view of the unsatisfactory treatment of cognitive disorders, in particular Alzheimer's disease (AD), the aim of this review was to perform a computer-aided analysis of the state of the art that will help in the search for innovative polypharmacology-based therapeutic approaches to fight against AD. Apart from 20-year unrenewed cholinesterase- or NMDA-based AD therapy, the hope of effectively treating Alzheimer's disease has been placed on serotonin 5-HT6 receptor (5-HT6R), due to its proven, both for agonists and antagonists, beneficial procognitive effects in animal models; however, research into this treatment has so far not been successfully translated to human patients. Recent lines of evidence strongly emphasize the role of kinases, in particular microtubule affinity-regulating kinase 4 (MARK4), Rho-associated coiled-coil-containing protein kinase I/II (ROCKI/II) and cyclin-dependent kinase 5 (CDK5) in the etiology of AD, pointing to the therapeutic potential of their inhibitors not only against the symptoms, but also the causes of this disease. Thus, finding a drug that acts simultaneously on both 5-HT6R and one of those kinases will provide a potential breakthrough in AD treatment. The pharmacophore- and docking-based comprehensive literature analysis performed herein serves to answer the question of whether the design of these kind of dual agents is possible, and the conclusions turned out to be highly promising.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output.

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

In Vitro and In Silico Analysis of the Residence Time of Serotonin 5-HT7 Receptor Ligands with Arylpiperazine Structure: A Structure-Kinetics Relationship Study.

During the last decade, the kinetics of drug-target interaction has received increasing attention as an important pharmacological parameter in the drug development process. Several studies have suggested that the lipophilicity of a molecule can play an important role. To date, this aspect has been studied for several G protein-coupled receptors (GPCRs) ligands but not for the 5-HT7 receptor (5-HT7R), a GPCR proposed as a valid therapeutic target in neurodevelopmental and neuropsychiatric disorders associated with abnormal neuronal connectivity. In this study, we report on structure-kinetics relationships of a set of arylpiperazine-based 5-HT7R ligands. We found that it is not the overall lipophilicity of the molecule that influences drug-target interaction kinetics but rather the position of polar groups within the molecule. Next, we performed a combination of molecular docking studies and molecular dynamics simulations to gain insights into structure-kinetics relationships. These studies did not suggest specific contact patterns between the ligands and the receptor-binding site as determinants for compounds kinetics. Finally, we compared the abilities of two 5-HT7R agonists with similar receptor-binding affinities and different residence times to stimulate the 5-HT7R-mediated neurite outgrowth in mouse neuronal primary cultures and found that the compounds induced the effect with different timing. This study provides the first insights into the binding kinetics of arylpiperazine-based 5-HT7R ligands that can be helpful to design new 5-HT7R ligands with fine-tuning of the kinetic profile.

Structural and Molecular Insight into Piperazine and Piperidine Derivatives as Histamine H3 and Sigma-1 Receptor Antagonists with Promising Antinociceptive Properties.

In an attempt to extend recent studies showing that some clinically evaluated histamine H3 receptor (H3R) antagonists possess nanomolar affinity at sigma-1 receptors (σ1R), we selected 20 representative structures among our previously reported H3R ligands to investigate their affinity at σRs. Most of the tested compounds interact with both sigma receptors to different degrees. However, only six of them showed higher affinity toward σ1R than σ2R with the highest binding preference to σ1R for compounds 5, 11, and 12. Moreover, all these ligands share a common structural feature: the piperidine moiety as the fundamental part of the molecule. It is most likely a critical structural element for dual H3/σ1 receptor activity as can be seen by comparing the data for compounds 4 and 5 (hH3R Ki = 3.17 and 7.70 nM, σ1R Ki = 1531 and 3.64 nM, respectively), where piperidine is replaced by piperazine. We identified the putative protein-ligand interactions responsible for their high affinity using molecular modeling techniques and selected compounds 5 and 11 as lead structures for further evaluation. Interestingly, both ligands turned out to be high-affinity histamine H3 and σ1 receptor antagonists with negligible affinity at the other histamine receptor subtypes and promising antinociceptive activity in vivo. Considering that many literature data clearly indicate high preclinical efficacy of individual selective σ1 or H3R ligands in various pain models, our research might be a breakthrough in the search for novel, dual-acting compounds that can improve existing pain therapies. Determining whether such ligands are more effective than single-selective drugs will be the subject of our future studies.

The Structural Determinants for α1-Adrenergic/Serotonin Receptors Activity among Phenylpiperazine-Hydantoin Derivatives.

Several studies confirmed the reciprocal interactions between adrenergic and serotoninergic systems and the influence of these phenomena on the pathogenesis of anxiety. Hence, searching for chemical agents with a multifunctional pharmacodynamic profile may bring highly effective therapy for CNS disorders. This study presents a deep structural insight into the hydantoin-arylpiperazine group and their serotonin/α-adrenergic activity. The newly synthesized compounds were tested in the radioligand binding assay and the intrinsic activity was evaluated for the selected derivatives. The computer-aided SAR analysis enabled us to answer questions about the influence of particular structural fragments on selective vs. multifunctional activity. As a result of the performed investigations, there were two leading structures: (a) compound 12 with multifunctional adrenergic-serotonin activity, which is a promising candidate to be an effective anxiolytic agent; (b) compound 14 with high α1A/α1D affinity and selectivity towards α1B, which is recommended due to the elimination of probable cardiotoxic effect. The structural conclusions of this work provide significant support for future lead optimization in order to achieve the desired pharmacodynamic profile in searching for new CNS-modulating agents.

How can SHAP values help to shape metabolic stability of chemical compounds?

BACKGROUND: Computational methods support nowadays each stage of drug design campaigns. They assist not only in the process of identification of new active compounds towards particular biological target, but also help in the evaluation and optimization of their physicochemical and pharmacokinetic properties. Such features are not less important in terms of the possible turn of a compound into a future drug than its desired affinity profile towards considered proteins. In the study, we focus on metabolic stability, which determines the time that the compound can act in the organism and play its role as a drug. Due to great complexity of xenobiotic transformation pathways in the living organisms, evaluation and optimization of metabolic stability remains a big challenge. RESULTS: Here, we present a novel methodology for the evaluation and analysis of structural features influencing metabolic stability. To this end, we use a well-established explainability method called SHAP. We built several predictive models and analyse their predictions with the SHAP values to reveal how particular compound substructures influence the model's prediction. The method can be widely applied by users thanks to the web service, which accompanies the article. It allows a detailed analysis of SHAP values obtained for compounds from the ChEMBL database, as well as their determination and analysis for any compound submitted by a user. Moreover, the service enables manual analysis of the possible structural modifications via the provision of analogous analysis for the most similar compound from the ChEMBL dataset. CONCLUSIONS: To our knowledge, this is the first attempt to employ SHAP to reveal which substructural features are utilized by machine learning models when evaluating compound metabolic stability. The accompanying web service for metabolic stability evaluation can be of great help for medicinal chemists. Its significant usefulness is related not only to the possibility of assessing compound stability, but also to the provision of information about substructures influencing this parameter. It can assist in the design of new ligands with improved metabolic stability, helping in the detection of privileged and unfavourable chemical moieties during stability optimization. The tool is available at https://metstab-shap.matinf.uj.edu.pl/ .

Novel N-Substituted 3-Aryl-4-(diethoxyphosphoryl)azetidin-2-ones as Antibiotic Enhancers and Antiviral Agents in Search for a Successful Treatment of Complex Infections.

A novel series of N-substituted cis- and trans-3-aryl-4-(diethoxyphosphoryl)azetidin-2-ones were synthesized by the Kinugasa reaction of N-methyl- or N-benzyl-(diethyoxyphosphoryl)nitrone and selected aryl alkynes. Stereochemistry of diastereoisomeric adducts was established based on vicinal H3-H4 coupling constants in azetidin-2-one ring. All the obtained azetidin-2-ones were evaluated for the antiviral activity against a broad range of DNA and RNA viruses. Azetidin-2-one trans-11f showed moderate inhibitory activity against human coronavirus (229E) with EC50 = 45 µM. The other isomer cis-11f was active against influenza A virus H1N1 subtype (EC50 = 12 µM by visual CPE score; EC50 = 8.3 µM by TMS score; MCC > 100 µM, CC50 = 39.9 µM). Several azetidin-2-ones 10 and 11 were tested for their cytostatic activity toward nine cancerous cell lines and several of them appeared slightly active for Capan-1, Hap1 and HCT-116 cells values of IC50 in the range 14.5-97.9 µM. Compound trans-11f was identified as adjuvant of oxacillin with significant ability to enhance the efficacy of this antibiotic toward the highly resistant S. aureus strain HEMSA 5. Docking and molecular dynamics simulations showed that enantiomer (3R,4S)-11f can be responsible for the promising activity due to the potency in displacing oxacillin at ß-lactamase, thus protecting the antibiotic from undesirable biotransformation.

Mutual Support of Ligand- and Structure-Based Approaches-To What Extent We Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors.

The process of modern drug design would not exist in the current form without computational methods. They are part of every stage of the drug design pipeline, supporting the search and optimization of new bioactive substances. Nevertheless, despite the great help that is offered by in silico strategies, the power of computational methods strongly depends on the input data supplied at the stage of the predictive model construction. The studies on the efficiency of the computational protocols most often focus on global efficiency. They use general parameters that refer to the whole dataset, such as accuracy, precision, mean squared error, etc. In the study, we examined machine learning predictions obtained for opioid receptors (mu, kappa, delta) and focused on cases for which the predictions were the most accurate and the least accurate. Moreover, by using docking, we tried to explain prediction errors. We attempted to develop a rule of thumb, which can help in the prediction of compound activity towards opioid receptors via docking, especially those that have been incorrectly predicted by machine learning. We found out that although the combination of ligand- and structure-based path can be beneficial for the prediction accuracy, there still remain cases that cannot be reliably predicted by any available modeling method. In addition to challenging ligand- and structure-based predictions, we also examined the role of the application of machine-learning methods in comparison to simple statistical methods for both standard ligand-based representations (molecular fingerprints) and interaction fingerprints. All approaches were confronted in both classification (where compounds were assigned to the group of active and inactive group constructed on the basis of Ki values) and regression (where exact Ki value was predicted) experiments.