Chemical substructures in drug discovery

Open-Source Approach to GPU-Accelerated Substructure Search

Chemical substructure search is a critical task in medicinal chemistry and small-molecule drug discovery, enabling the retrieval of molecules from databases based on specific chemical features. While systems exist for this purpose, the challenge of efficient and swift searching persists, particularly as data storage migrates to the cloud, introducing new complexities. This study provides a comprehensive analysis of chemical substructure searches, showcasing the benefits of graphics processing unit-accelerated fingerprint screening. The research highlights strategies for optimizing performance, making significant advancements in substructure searching, a pivotal aspect of drug discovery and molecular research. The accessible and scalable nature of the proposed approach makes it a valuable resource for scientists aiming to enhance their substructure search capabilities.

Single-step retrosynthesis prediction by leveraging commonly preserved substructures

Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where commonly preserved portions of product molecules were automatically extracted with a fully data-driven approach. Our model achieves improvement over previously reported models, and we demonstrate that its performance can be boosted further by enhancing the accuracy of these substructures. Analyzing substructures extracted from our machine learning model can provide human experts with additional insights to assist decision-making in retrosynthesis analysis.

DeepIDC: A Prediction Framework of Injectable Drug Combination Based on Heterogeneous Information and Deep Learning

BACKGROUND AND OBJECTIVE

In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). The clinical application of injections is increasing, and many injections lack relevant combination information. It is still a significant need for experienced clinical pharmacists to participate in evidence-based drug decision making, monitor medication safety, and manage drug interactions. Meanwhile, a large number of injection pairs and dosage combinations limit exhaustive screening. Here, we present a prediction framework, called DeepIDC, that can expediently screen the feasibility of IDCs using heterogeneous information with deep learning. This is the first specific prediction framework to identify IDCs.

METHODS

Since the interaction between the injected drugs may occur in the direct physical and chemical reactions at the time of mixing or may be the indirect interaction of their drug targets and pathways, we used molecular fingerprints, drug-target associations, and drug-pathway associations to convert injections into a string of digital vectors. Then, based on these injection vectors, we combined a bidirectional long short-term memory and a feed-forward neural network to build a prediction model for accurate and instructive prediction of IDC.

RESULTS

In three realistic evaluation scenarios, DeepIDC has achieved ideal prediction results. Furthermore, compared with the other five machine-learning methods, the proposed predictor is more efficient and robust. Among the top 30 potential IDCs of each IDC class predicted by DeepIDC, we found that 9 cases were experimentally verified in the literature or available on Drug.com.

CONCLUSION

The information we extracted in vivo and in vitro can effectively characterize injectable drugs. DeepIDC developed based on deep learning algorithm provides a valuable unified framework for new IDC discovery, which can make up for the lack of IDC information and predict potential IDC events.

Saagar-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions

Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures, Saagar, to support interpretable predictive models and read-across protocols. Performance of Saagar in chemical characterization and search for structurally similar actives for read-across applications was compared with four publicly available fingerprint sets (MACCS (166), PubChem (881), ECFP4 (1024), ToxPrint (729)) in three benchmark sets (MUV, ULS, and Tox21) spanning ∼145 000 compounds and 78 molecular targets at 1%, 2%, 5%, and 10% false discovery rates. In 18 of the 20 comparisons, interpretable Saagar features performed better than the publicly available, but less interpretable and fixed-bit length, fingerprints. Examples are provided to show the enhanced capability of Saagar in extracting compounds with higher scaffold similarity. Saagar features are interpretable and efficiently characterize diverse chemical collections, thus making them a better choice for building interpretable predictive in silico models and read-across protocols.

Consensus-Based Pharmacophore Mapping for New Set of N-(disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides

A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene- 2-carboxamide derivatives was synthesized and characterized as potential antimicrobial agents. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[2-chloro-5-(trifluoromethyl)phenyl]-3-hydroxy- naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68 µM) activity against methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[4-bromo-3-(trifluoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity against M. tuberculosis (both MICs 10 µM) comparable with that of rifampicin. Synergistic activity was observed for the combinations of ciprofloxacin with N-[4-bromo-3-(trifluoromethyl)phenyl]- and N-(4-bromo-3-fluorophenyl)-3-hydroxynaphthalene-2-carboxamides against MRSA SA 630 isolate. The similarity-related property space assessment for the congeneric series of structurally related carboxamide derivatives was performed using the principal component analysis. Interestingly, different distribution of mono-halogenated carboxamide derivatives with the –CF₃ substituent is accompanied by the increased activity profile. A symmetric matrix of Tanimoto coefficients indicated the structural dissimilarities of dichloro- and dimetoxy-substituted isomers from the remaining ones. Moreover, the quantitative sampling of similarity-related activity landscape provided a subtle picture of favorable and disallowed structural modifications that are valid for determining activity cliffs. Finally, the advanced method of neural network quantitative SAR was engaged to illustrate the key 3D steric/electronic/lipophilic features of the ligand-site composition by the systematic probing of the functional group.

Stat3 regulates centrosome clustering in cancer cells via Stathmin/PLK1

Cancer cells frequently have amplified centrosomes that must be clustered together to form a bipolar mitotic spindle, and targeting centrosome clustering is considered a promising therapeutic strategy. A high-content chemical screen for inhibitors of centrosome clustering identified Stattic, a Stat3 inhibitor. Stat3 depletion and inhibition in cancer cell lines and in tumours in vivo caused significant inhibition of centrosome clustering and viability. Here we describe a transcription-independent mechanism for Stat3-mediated centrosome clustering that involves Stathmin, a Stat3 interactor involved in microtubule depolymerization, and the mitotic kinase PLK1. Furthermore, PLK4-driven centrosome amplified breast tumour cells are highly sensitive to Stat3 inhibitors. We have identified an unexpected role of Stat3 in the regulation of centrosome clustering, and this role of Stat3 may be critical in identifying tumours that are sensitive to Stat3 inhibitors.

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations

Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

HIM-herbal ingredients in-vivo metabolism database

Background

Herbal medicine has long been viewed as a valuable asset for potential new drug discovery and herbal ingredients’ metabolites, especially the in vivo metabolites were often found to gain better pharmacological, pharmacokinetic and even better safety profiles compared to their parent compounds. However, these herbal metabolite information is still scattered and waiting to be collected.

Description

HIM database manually collected so far the most comprehensive available in-vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile. Currently HIM contains 361 ingredients and 1104 corresponding in-vivo metabolites from 673 reputable herbs. Tools of structural similarity, substructure search and Lipinski’s Rule of Five are also provided. Various links were made to PubChem, PubMed, TCM-ID (Traditional Chinese Medicine Information database) and HIT (Herbal ingredients’ targets databases).

Conclusions

A curated database HIM is set up for the in vivo metabolites information of the active ingredients for Chinese herbs, together with their corresponding bioactivity, toxicity and ADME profile. HIM is freely accessible to academic researchers at http://www.bioinformatics.org.cn/.

Crowd computing: using competitive dynamics to develop and refine highly predictive models

A recent application of a crowd computing platform to develop highly predictive in silico models for use in the drug discovery process is described. The platform, Kaggle™, exploits a competitive dynamic that results in model optimization as the competition unfolds. Here, this dynamic is described in detail and compared with more-conventional modeling strategies. The complete and full structure of the underlying dataset is disclosed and some thoughts as to the broader utility of such 'gamification' approaches to the field of modeling are offered.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Correction for Interference by Test Samples in High-Throughput Assays

In high-throughput biochemical assays performed in multiwell plates, the effect of test samples on the activity of the biochemical system is usually measured by optical means such as absorbance, fluorescence, luminescence, or scintillation counting. The test sample often causes detection interference when it remains in the well during the measurement. Interference may be due to light absorption, fluorescence quenching, sample fluorescence, chemical interaction of the sample with a detection reagent, or depression of the meniscus. A simple method is described that corrects for such interference well by well. The interference is measured in a separate artifact assay plate. An appropriate arithmetic correction is then applied to the measurement in the corresponding well of the activity assay plate. The correction procedure can be used for single-point screening or potency measurements on serial dilutions of test samples.

Design, selection, and characterization of thioflavin-based intercalation compounds with metal chelating properties for application in Alzheimer's disease

Metal chelation is considered a rational therapeutic approach for interdicting Alzheimer's amyloid pathogenesis. At present, enhancing the targeting and efficacy of metal-ion chelating agents through ligand design is a main strategy in the development of the next generation of metal chelators. Inspired by the traditional dye Thioflavin-T, we have designed new multifunctional molecules that contain both amyloid binding and metal chelating properties. In silico techniques have enabled us to identify commercial compounds that enclose the designed molecular framework (M1), include potential antioxidant properties, facilitate the formation of iodine-labeled derivatives, and can be permeable through the blood-brain barrier. Iodination reactions of the selected compounds, 2-(2-hydroxyphenyl)benzoxazole (HBX), 2-(2-hydroxyphenyl)benzothiazole (HBT), and 2-(2-aminophenyl)-1H-benzimidazole (BM), have led to the corresponding iodinated derivatives HBXI, HBTI, and BMI, which have been characterized by X-ray diffraction. The chelating properties of the latter compounds toward Cu(II) and Zn(II) have been examined in the solid phase and in solution. The acidity constants of HBXI, HBTI, and BMI and the formation constants of the corresponding ML and ML2 complexes [M = Cu(II), Zn(II)] have been determined by UV-vis pH titrations. The calculated values for the overall formation constants for the ML2 complexes indicate the suitability of the HBXI, HBTI, and BMI ligands for sequestering Cu(II) and Zn(II) metal ions present in freshly prepared solutions of beta-amyloid (Abeta) peptide. This was confirmed by Abeta aggregation studies showing that these compounds are able to arrest the metal-promoted increase in amyloid fibril buildup. The fluorescence features of HBX, HBT, BM, and the corresponding iodinated derivatives, together with fluorescence microscopy studies on two types of pregrown fibrils, have shown that HBX and HBT compounds could behave as potential markers for the presence of amyloid fibrils, whereas HBXI and HBTI may be especially suitable for radioisotopic detection of Abeta deposits. Taken together, the results reported in this work show the potential of new multifunctional thioflavin-based chelating agents as Alzheimer's disease therapeutics.

Identification and selection of "privileged fragments" suitable for primary screening

The use of small molecule libraries for fragment-based primary screening (FBS) is a well-known approach to identify protein binders in the low affinity range. However, the search, analysis, and selection of suitable screening fragments can be a lengthy process, because of the large number of compounds that must be analyzed for different levels of ring/substituents identification and submitted to selection/exclusion criteria based on their physicochemical properties. The purpose of the present work is to propose a strategy to identify substructures from databases of known drugs, which can be used as templates for the generation of libraries of "privileged fragments" that are able to provide high-quality hits. The entire process has been developed integrating Pipeline Pilot (Accelrys Inc., San Diego, CA; http://www.accelrys.com ) native components and user-defined molecular files containing ISIS-like substructure query features (Symyx, San Ramon, CA; http://www.symyx.com ). The method is effortless, easy to put in place, and fast enough to be iteratively applied to different sources of druglike compounds.

In silico screening of HIV-1 non-nucleoside reverse transcriptase and protease inhibitors

Two targets, reverse transcriptase (RT) and protease from HIV-1, were used during the past two decades to the discovery of non-nucleoside reverse transcriptase inhibitors (NNRTI) and protease inhibitors (PI) that belong to the arsenal of the antiretroviral therapy. Herein these enzymes were chosen as templates for conducting a computer-aided ligand design. Ligand and structure-based drug designs were the starting points to select compounds from a database bearing more than five million compounds by means of cheminformatic tools. New promising lead structures are retrieved from the database, which are open to acquisition and test. Classes of molecules already described as NNRTI or PI in the literature also came out and were useful to prove the reliability of the workflow, and thus validating the work carried out so far.

New similarity-based algorithm and its application to classification of anticonvulsant compounds

A similarity-based algorithm based on a previously developed model is applied in the classification of two sets of anticonvulsant and non-anticonvulsant drugs. Each set is composed of a) anticonvulsant compounds that have shown moderate to high activity in the Maximal Electroshock Seizure (MES) test and b) drugs with other biological activities or poor activity in the MES test. The results from the analysis of variance (ANOVA) indicate that the proposed algorithm is able to differentiate anticonvulsant from non-anticonvulsant drugs. The proposed model may then be useful in the identification of new anticonvulsant agents through virtual screening of large virtual libraries of chemical structures.

Mining of Randomly Generated Molecular Fragment Populations Uncovers Activity-Specific Fragment Hierarchies

We introduce a methodology to analyze random molecular fragment populations and determine conditional probability relationships between fragments. Random fragment profiles are generated for an arbitrary set of molecules, and each observed fragment is assigned a frequency vector. An algorithm is designed to compare frequency vectors and derive dependencies of fragment occurrence. Using calculated dependency values, random fragment populations can be organized in graphs that capture their relationships and make it possible to map fragment pathways of biologically active molecules. For sets of molecules having similar activity, unique fragment signatures are identified. The analysis reveals that random fragment profiles contain compound class-specific information and provides evidence for the existence of activity-specific fragment hierarchies.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data

MOTIVATION

Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability.

RESULTS

We describe a novel method for predicting protein-chemical interaction using SVM. We utilize very general protein data, i.e. amino acid sequences, and combine these with chemical structures and mass spectrometry (MS) data. MS data can be of great use in finding new chemical compounds in the future. We assessed the validity of our method in the dataset of the binding of existing drugs and found that more than 80% accuracy could be obtained. Furthermore, we conducted comprehensive target protein predictions for MDMA, and validated the biological significance of our method by successfully finding proteins relevant to its known functions.

AVAILABILITY

Available on request from the authors.

A Cluster-Based Strategy for Assessing the Overlap between Large Chemical Libraries and Its Application to a Recent Acquisition

We report on the structural comparison of the corporate collections of Johnson & Johnson Pharmaceutical Research & Development (JNJPRD) and 3-Dimensional Pharmaceuticals (3DP), performed in the context of the recent acquisition of 3DP by JNJPRD. The main objective of the study was to assess the druglikeness of the 3DP library and the extent to which it enriched the chemical diversity of the JNJPRD corporate collection. The two databases, at the time of acquisition, collectively contained more than 1.1 million compounds with a clearly defined structural description. The analysis was based on a clustering approach and aimed at providing an intuitive quantitative estimate and visual representation of this enrichment. A novel hierarchical clustering algorithm called divisive k-means was employed in combination with Kelley's cluster-level selection method to partition the combined data set into clusters, and the diversity contribution of each library was evaluated as a function of the relative occupancy of these clusters. Typical 3DP chemotypes enriching the diversity of the JNJPRD collection were catalogued and visualized using a modified maximum common substructure algorithm. The joint collection of JNJPRD and 3DP compounds was also compared to other databases of known medicinally active or druglike compounds. The potential of the methodology for the analysis of very large chemical databases is discussed.

SVM approach for predicting LogP

The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

Assessment of Molecular Similarity from the Analysis of Randomly Generated Structural Fragment Populations

A novel method termed MolBlaster is introduced for the evaluation of molecular similarity relationships on the basis of randomly generated fragment populations. Our motivation has been to develop a similarity method that does not depend on the use of predefined structural or property descriptors. Fragment profiles of molecules are generated by random deletion of bonds in connectivity tables and quantitatively compared using entropy-based metrics. In test calculations, MolBlaster accurately reproduced a structural key-based similarity ranking of druglike molecules.

Anatomy of fingerprint search calculations on structurally diverse sets of active compounds

Similarity searching using molecular fingerprints is a widely used approach for the identification of novel hits. A fingerprint search involves many pairwise comparisons of bit string representations of known active molecules with those precomputed for database compounds. Bit string overlap, as evaluated by various similarity metrics, is used as a measure of molecular similarity. Results of a number of studies focusing on fingerprints suggest that it is difficult, if not impossible, to develop generally applicable search parameters and strategies, irrespective of the compound classes under investigation. Rather, more or less, each individual search problem requires an adjustment of calculation conditions. Thus, there is a need for diagnostic tools to analyze fingerprint-based similarity searching. We report an analysis of fingerprint search calculations on different sets of structurally diverse active compounds. Calculations on five biological activity classes were carried out with two fingerprints in two compound source databases, and the results were analyzed in histograms. Tanimoto coefficient (Tc) value ranges where active compounds were detected were compared to the distribution of Tc values in the database. The analysis revealed that compound class-specific effects strongly influenced the outcome of these fingerprint calculations. Among the five diverse compound sets studied, very different search results were obtained. The analysis described here can be applied to determine Tc intervals where scaffold hopping occurs. It can also be used to benchmark fingerprint calculations or estimate their probability of success.

Mapping Algorithms for Molecular Similarity Analysis and Ligand-Based Virtual Screening: Design of DynaMAD and Comparison with MAD and DMC

Here, we introduce the DynaMAD algorithm that is designed to map database compounds to combinations of activity-class-dependent descriptor value ranges in order to identify novel active molecules. The method combines and extends key features of two previously developed algorithms, MAD and DMC. These methods were first described as compound-mapping algorithms for large-scale virtual screening applications. DynaMAD and DMC operate in chemical spaces of stepwise increasing dimensionality. However, in contrast to DMC, which utilizes binary transformed descriptors, DynaMAD uses unmodified descriptor value distributions. The performance of these mapping methods was compared in detail in virtual screening trials on 24 different compound activity classes against a background of about 2 million database compounds. In these calculations, all three approaches produced results of considerable predictive value, and the enrichment of active molecules in small selection sets consisting of only about 20 or fewer database compounds emerged as a common feature. Furthermore, mapping methods were capable of recognizing remote molecular similarity relationships. Overall, DynaMAD performed better than MAD and DMC, producing average hit and recovery rates of 55% and 33%, respectively, over all 24 classes. Taken together, our findings suggest that dynamic compound mapping to combinations of activity-class-selective descriptor settings has significant potential for molecular similarity analysis and ligand-based virtual screening.

Assay Development and Screening of a Serine/Threonine Kinase in an On-Chip Mode Using Caliper Nanofluidics Technology

Kinases are key targets for drug discovery. In the field of screening in general and especially in the kinase area, because of considerations of efficiency and cost, radioactivity-based assays tend to be replaced by alternative, mostly fluorescence-based, assays. Today, the limiting factor is rarely the number of data points that can be obtained but rather the quality of the data, enzyme availability, and cost. In this article, the authors describe the development of an assay for a kinase screen based on the electrophoretic separation of fluorescent product and substrate using a Caliper-based nanofluidics environment in on-chip incubation mode. The authors present the results of screening a focused set of 32,000 compounds together with confirmation data obtained in a filtration assay. In addition, they have made a small-scale comparison between the on-chip and off-chip nanofluidics screening modes. In their hands, the screen in on-chip mode is characterized by high precision most likely due to the absence of liquid pipetting; an excellent confirmation rate (62%) in an independent assay format, namely, filtration; and good sensitivity. This study led to the identification of 4 novel chemical series of inhibitors.

Effect of cholesterol on DMPC phospholipid membranes and QSAR model construction in membrane-interaction QSAR study through molecular dynamics simulation

In this study, both pure DMPC and DMPC/cholesterol mixed membrane monolayer were built to compare the physical-chemical properties and dynamics properties through molecular dynamics simulation and normal-mode analysis. The results show that the addition of cholesterol decreases the area of per molecule of membrane, increases the lipid amplitude motion, and changes the solute diffusion coefficient. It is also found that the addition of cholesterol greatly changes the solute-membrane 1,4-nonbonded interaction energy (deltaE14). MI-QSAR models were constructed based on solute-membrane interaction energy descriptors and other intramolecular descriptors. The results show that deltaE14 substitutes deltaE(HB) as the second important descriptor compared with the previous study. Final results suggest that short range solute-membrane interaction energy changes due to the uptake of the solute may play an important decision on permeability in DMPC/cholesterol membrane. A test set was applied to evaluate the predictivity of MI-QSAR models. The result suggests that the combination of F(H2O) and deltaE14 not only improves r2 and q2, but also greatly improves the model predictivity. Based on the combination of q2 and r(pre)2 values, a two-term model is better used to predict the solute permeability in this study (r2 = 0.859, q2 = 0.803, and r(pre)2 = 0.540). Due to the small sample both in training set and test set, more datasets are necessary to make a final decision about the model construction and prediction.

Are Target-Family-Privileged Substructures Truly Privileged?

One of the early and effective approaches to G-coupled protein receptor target family library design was the analysis of a set of ligands for frequently occurring chemical moieties or substructures. Various methods ranging from frameworks analysis to pharmacophores have been employed to find these so-called target-family-privileged substructures. Although the use of these substructures is common practice in combinatorial library design and has produced leads, the methods used for finding them rarely verified their selectivity for the particular target family from which they were derived. The frequency of occurrence among ligands associated with a target receptor family is not a sufficient criterion for those substructures to receive the label of target-family-privileged substructure. This study explores the question of selectivity of ClassPharmer generated fragments for a series of target families: GPCRs, nuclear hormone receptors, serine proteases, protein kinases, and ligand-gated ion channels. In addition, a GPCR focused library and a random set of 10k compounds are examined in terms of their target-family-privileged-substructure composition. The results challenge the combinatorial chemistry concept of target-family-privileged substructures and suggest that many of these fragments may simply be drug-like or attractive for various receptors in accordance with the original definition of privileged substructures.

High-throughput pharmacokinetic method: cassette dosing in mice associated with minuscule serial bleedings and LC/MS/MS analysis

A method for pharmacokinetic studies using cassette dosing associated with serial bleeding in mice is described. PK profiles of four soluble epoxide hydrolase inhibitors were determined following oral, subcutaneous or intraperitoneal administration individually or in cassette dosing. Parent analyses were performed on only 5 microL of whole blood from serial bleeds (up to 10 per animal), by LC/MS/MS. An accuracy (88-100%) and precision (<10% RSD) were observed, leading to reliable datum points for PK calculation. PK profiles, T(max), C(max) and half-life values after cassette dosing were similar to the individual PK results. This method dramatically increases speed of data collection while dramatically reducing cost and animal usage. The results presented here clearly indicate that this proposed method could be applicable to high-throughput PK studies.

Discovery and development of a new class of potent, selective, orally active oxytocin receptor antagonists

We report a novel chemical class of potent oxytocin receptor antagonists showing a high degree of selectivity against the closely related vasopressin receptors (V1a, V1b, V2). An initial compound, 7, was shown to be active in an animal model of preterm labor when administered by the intravenous but not by the oral route. Stepwise SAR investigations around the different structural elements revealed one position, the arenesulfonyl moiety, to be amenable to structural changes. Consequently, this position was used to introduce a variety of substituents to improve the physicochemical properties. Some of the resulting analogues were found to be superior to 7 both in terms of potency in vitro and aqueous solubility, which translated into significantly improved efficacy in the animal model after intravenous and oral administration. The best compound, 73, potently inhibited oxytocin-induced uterine contractions in nonpregnant rats and reduced spontaneous uterine contractions in late-term pregnant rats.

Similarity search profiling reveals effects of fingerprint scaling in virtual screening

Fingerprint scaling is a method to increase the performance of similarity search calculations. It is based on the detection of bit patterns in keyed fingerprints that are signatures of specific compound classes. Application of scaling factors to consensus bits that are mostly set on emphasizes signature bit patterns during similarity searching and has been shown to improve search results for different fingerprints. Similarity search profiling has recently been introduced as a method to analyze similarity search calculations. Profiles separately monitor correctly identified hits and other detected database compounds as a function of similarity threshold values and make it possible to estimate whether virtual screening calculations can be successful or to evaluate why they fail. This similarity search profile technique has been applied here to study fingerprint scaling in detail and better understand effects that are responsible for its performance. In particular, we have focused on the qualitative and quantitative analysis of similarity search profiles under scaling conditions. Therefore, we have carried out systematic similarity search calculations for 23 biological activity classes under scaling conditions over a wide range of scaling factors in a compound database containing approximately 1.3 million molecules and monitored these calculations in similarity search profiles. Analysis of these profiles confirmed increases in hit rates as a consequence of scaling and revealed that scaling influences similarity search calculations in different ways. Based on scaled similarity search profiles, compound sets could be divided into different categories. In a number of cases, increases in search performance under scaling conditions were due to a more significant relative increase in correctly identified hits than detected false-positives. This was also consistent with the finding that preferred similarity threshold values increased due to fingerprint scaling, which was well illustrated by similarity search profiling.

An Automated Method for Exploring Targeted Substructural Diversity within Sets of Chemical Structures

Practicing medicinal chemists tend to treat a lead compound as an assemblage of its substructural parts. By iteratively confining their synthetic efforts in a localized fashion, they are able to systematically investigate how minor changes in certain portions of the molecule effect the properties of interest in the logical expectation that the observed beneficial changes will be cumulative. One disadvantage to this approach arises when large amounts of structure data begin to accumulate which is often the case in recent times due to such developments as high-throughput screening, virtual screening, and combinatorial chemistry. How then does one interactively mine this diverse data consistent with the desired substructural template, so those desirable structural features can be discovered and interpreted, especially when they may not occur in the most active compounds due to structural deficiencies in other portions of the molecule? In this paper, we present an algorithm to automate this process that has historically been performed in an ad-hoc and manual fashion. Using the proposed method, significantly larger numbers of compounds can be analyzed in this fashion, potentially discovering useful structural feature combinations that would not have otherwise been detected due to the sheer scale of modern structural and biological data collections.

Exploring the chemogenomic knowledge space with annotated chemical libraries

The recent human genome initiatives have led to the discovery of a multitude of genes that are potentially associated with various pathologic conditions and, thus, have opened new horizons in drug discovery. Simultaneously, annotated chemical libraries have emerged as information-rich databases to integrate biological and chemical data. They can be useful for the discovery of new pharmaceutical leads, the validation of new biotargets and the determination of the structural basis of ligand selectivity within target families. Annotated libraries provide a strong information basis for computational design of target-directed combinatorial libraries, which are a key component of modern drug discovery. Today, the rational design of chemical libraries enhanced with chemogenomics data is a new area of progressive research.

Multicomponent Reactions with Dihydroazines: Efficient Synthesis of a Diverse Set of Pyrido-Fused Tetrahydroquinolines

A multicomponent assembly of pyrido-fused tetrahydroquinolines is accomplished in a one-pot process from the interaction of dihydroazines, aldehydes, and anilines. A rational screening of the different components and parameters of this reaction, such as the range of reactive starting materials, catalysts and reaction conditions (solvent range; thermal, high pressure- and microwave-promoted processes) is carried out. Optimized conditions allow an efficient preparation of pyrido-fused tetrahydroquinolines with good yields, bypassing the biomimetic NADH-like reductive pathway which is typical in the interaction of dihydropyridines with carbonyl compounds and amines. Furthermore, solid-supported versions of the process have been developed, which should facilitate the preparation of libraries.