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PubMed2000

Predicting blood-brain barrier permeation from three-dimensional molecular structure.

Predicting blood-brain barrier (BBB) permeation remains a challenge in drug design. Since it is impossible to determine experimentally the BBB partitioning of large numbers of preclinical candidates, alternative evaluation methods based on computerized models are desirable. The present study was conducted to demonstrate the value of descriptors derived from 3D molecular fields in estimating the BBB permeation of a large set of compounds and to produce a simple mathematical model suitable for external prediction. The method used (VolSurf) transforms 3D fields into descriptors and correlates them to the experimental permeation by a discriminant partial least squares procedure. The model obtained here correctly predicts more than 90% of the BBB permeation data. By quantifying the favorable and unfavorable contributions of physicochemical and structural properties, it also offers valuable insights for drug design, pharmacological profiling, and screening. The computational procedure is fully automated and quite fast. The method thus appears as a valuable new tool in virtual screening where selection or prioritization of candidates is required from large collections of compounds.

Blood-Brain Barrier↗

PubMed2004

Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow.

The paper is illustrating how the general data mining methodology may be adapted to provide solutions to the problem of high throughput virtual screening of organic chemicals for possible acute toxicity to the fathead minnow fish. The present approach involves mining fragment information from chemical structures and is using probabilistic neural networks to model the relationship between structure and toxicity. Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models. The mathematical principles supporting their use for value prediction purposes are clarified and their peculiarities discussed. As part of the research phase of the data mining process, a dataset consisting of 800 structures and associated fathead minnow (Pimephales promelas) 96-h LC50 acute toxicity endpoint information is used for both the purpose of identifying an advantageous combination of fragment descriptors and for training the neural networks. As a result, two powerful models are generated. Model 1 implements the basic PNN with Gaussian kernel (statistical corrections included) while Model 2 implements the PNN with Gaussian kernel and separated variables. External validation is performed using a separate dataset consisting of 86 structures and associated toxicity information. Both learning and generalization capabilities of the two models are investigated and their limitations discussed.

Animals↗

PubMedDate not supplied

Screening drug-like compounds by docking to homology models: a systematic study.

In the absence of an experimentally solved structure, a homology model of a protein target can be used instead for virtual screening of drug candidates by docking and scoring. This approach poses a number of questions regarding the choice of the template to use in constructing the model, the accuracy of the screening results, and the importance of allowing for protein flexibility. The present study addresses such questions with compound screening calculations for multiple homology models of five drug targets. A central result is that docking to homology models frequently yields enrichments of known ligands as good as that obtained by docking to a crystal structure of the actual target protein. Interestingly, however, standard measures of the similarity of the template used to build the homology model to the targeted protein show little correlation with the effectiveness of the screening calculations, and docking to the template itself often is as successful as docking to the corresponding homology model. Treating key side chains as mobile produces a modest improvement in the results. The reasons for these sometimes unexpected results, and their implications for future methodologic development, are discussed.

Computer Simulation↗

PubMed2005

Library design for fragment based screening.

According to Hann's model of molecular complexity an increased probability of detection binding to a target protein can be expected when small, low complex molecular fragments are screened with high sensitivity instead of full-sized ligands with lower sensitivity. Analysis of the HTS summary data of Novartis and comparison with NMR screening results obtained on generic fragment libraries indicate this expectation to be true with hitrates of 0.001% - 0.151% observed in the identification of ligands with an IC(50) threshold in the micromolar range in an HTS setup and hitrates above or equal to 3% observed in NMR screening of fragments with an affinity threshold in the millimolar range. It is however necessary to keep in mind that the sets of target studied were not identical for both method and the experience in NMR screening is too limited for a final conclusion. The term hitrate as used here reflects only the success rate in the observation of ligand binding event. It must not be confused with the overall success rate of fragment and high throughput screening in the lead finding process, which can be entirely different, since the steps required to follow-up a ligand binding event to a lead are different for both methods. A survey of fragment-based lead discovery case studies given in the literature shows that in approximately half of the cases the initial hit fragment was discovered by screening a generic library, whereas in the other cases some knowledge about an initial ligands or the protein binding site has been used, whereas systematic virtual screening of fragment databases has been only rarely reported. As comparatively high hitrates were obtained, further consideration to optimize the generic fragment screening library were directed to the chemical tractability of the fragment. As several functional groups preferred by chemists for modification and linking of the fragments are also preferentially involved in interactions between the fragments and the target protein, a set of screening fragments was derived from chemical building blocks by masking its linker group by a chemical transformation which can be later on used in the chemical follow-up of the fragment hit. For example primary amines can be masked as acetamides. If the screening fragment is active the related building block can then be used for synthesis of a follow-up library.

Combinatorial Chemistry Techniques↗

PubMed2004

Flat colorectal lesions in asymptomatic adults: implications for screening with CT virtual colonoscopy.

OBJECTIVE: The clinical significance of flat lesions in colorectal cancer screening remains uncertain. The purpose of this study was to investigate the frequency, histology, and virtual colonoscopy detection of flat lesions in an asymptomatic screening population. SUBJECTS AND METHODS: The morphology of all detected polyps was prospectively recorded as flat or polypoid (sessile or pedunculated) in 1,233 consecutive asymptomatic adults who underwent same-day virtual colonoscopy and optical colonoscopy. A flat morphology was defined as a shallow plaquelike broad-based lesion with a height of less than one half of its width. RESULTS: Of 344 polyps of 6 mm or greater confirmed at optical colonoscopy, 17 (4.9%) were labeled as flat at both virtual colonoscopy and optical colonoscopy; 17 (4.9%), at optical colonoscopy only; and 25 (7.3%), at virtual colonoscopy only, yielding 59 total lesions in 52 (4.2%) of 1,233 patients. Twenty-nine (49.2%) of 59 flat lesions were adenomatous, of which four measured 10 mm or greater and one 6- to 9-mm lesion was histologically advanced. None of the 148 diminutive flat lesions (< or = 5 mm) detected at optical colonoscopy was histologically advanced. Virtual colonoscopy prospectively detected 24 (82.8%) of 29 flat adenomas and 47 (80.0%) of all 59 flat lesions 6 mm or greater. In comparison, the sensitivity of virtual colonoscopy for the detection of polypoid adenomas and all polypoid lesions of 6 mm or greater was 86.2% (156/181, p = 0.58) and 81.0% (231/285, p = 0.86), respectively. CONCLUSION: Flat adenomas measuring 6 mm or greater are uncommon in a typical Western screening population, and advanced flat neoplasms are rare. The sensitivity of virtual colonoscopy for detecting flat lesions was similar to that of polypoid lesions. These results indicate that flat lesions are not a significant drawback for virtual colonoscopy screening.

Adenoma↗

PubMed2004

Ligand identification for G-protein-coupled receptors: a lead generation perspective.

This review addresses strategies for the generation of ligands for G-protein-coupled receptors outside classical high-throughput screening and literature based approaches. These range from the chemical intuition-based strategies of endogenous ligand elaboration and privileged structure decoration to the in silico approaches of virtual screening and de novo design. Examples are cited where supporting pharmacological data has been presented.

Combinatorial Chemistry Techniques↗

PubMed2002

A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening.

High-throughput and virtual screening are widely used to discover novel leads for drug design. On examination, many screening hits appear non-drug-like: they act noncompetitively, show little relationship between structure and activity, and have poor selectivity. Attempts to develop these peculiar molecules into viable leads are often futile, and much time can be wasted on the characterization of these "phony" hits. Despite their common occurrence, the mechanism of action of these promiscuous molecules remains unknown. To investigate this problem, 45 diverse screening hits were studied. Fifteen of these were previously reported as inhibitors of various receptors, including beta-lactamase, malarial protease, dihydrofolate reductase, HIV Tar RNA, thymidylate synthase, kinesin, insulin receptor, tyrosine kinases, farnesyltransferase, gyrase, prions, triosephosphate isomerase, nitric oxide synthase, phosphoinositide 3-kinase, and integrase; 30 were from an in-house screening library of a major pharmaceutical company. In addition to their original targets, 35 of these 45 compounds were shown to inhibit several unrelated model enzymes. These 35 screening hits included compounds, such as fullerenes, dyes, and quercetin, that have repeatedly shown activity against diverse targets. When tested against the model enzymes, the compounds showed time-dependent but reversible inhibition that was dramatically attenuated by albumin, guanidinium, or urea. Surprisingly, increasing the concentration of the model enzymes 10-fold largely eliminated inhibition, despite a 1000-fold excess of inhibitor; a well-behaved competitive inhibitor did not show this behavior. One model to explain these observations was that the active form of the promiscuous inhibitors was an aggregate of many individual molecules. To test this hypothesis, light scattering and electron microscopy experiments were performed. The nonspecific inhibitors were observed to form particles of 30-400 nm diameter by both techniques. In control experiments, a well-behaved competitive inhibitor and an inactive dye-like molecule were not observed to form aggregates. Consistent with the hypothesis that the aggregates are the inhibitory species, the particle size and IC(50) values of the promiscuous inhibitors varied monotonically with ionic strength; a competitive inhibitor was unaffected by changes in ionic strength. Unexpectedly, aggregate formation appears to explain the activity of many nonspecific inhibitors and may account for the activity of many promiscuous screening hits. Molecules acting via this mechanism may be widespread in drug discovery screening databases. Recognition of these compounds may improve screening results in many areas of pharmaceutical interest.

Dialysis↗

PubMed2002

Integration of virtual and high-throughput screening.

High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.

Drug Design↗

PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗

PubMed2005

Prospects for combinatorial chemistry in the agrosciences.

The recent progress and future prospects for the successful application of combinatorial chemistry and high throughput screening within the agrochemical lead discovery process are outlined and discussed. Solid and solution phase library synthesis technologies are reviewed and compared, and the role and importance of bioavailability, diversity and virtual screening in rational library design are detailed.

Agrochemicals↗

PubMed2005

Screening for dihydrofolate reductase inhibitors using MOLPRINT 2D, a fast fragment-based method employing the naïve Bayesian classifier: limitations of the descriptor and the importance of balanced chemistry in training and test sets.

A fragment-based similarity searching method, MOLPRINT 2D, was employed for virtual screening of Escherichia coli dihydrofolate reductase inhibitors. Using the original training set of 50,000 compounds, only marginal enrichment factors (between 1 and 3) could be achieved on the test library. The active structures contained in the training and test libraries represented different types of "chemistry", that is, different substructural features associated with activity. Training and test sets were pooled in a 2nd step and randomly split into training and test of equal size, with the objective of smoothing out the different chemical characteristics of both libraries. In a 10-fold cross-validation study on the new training and test sets, typically 10-fold enrichment could be found in the first 96 positions, 4-fold enrichment in the first 384 positions, and 3-fold enrichment in the first 1536 positions, corresponding to 6, 10, and 28 hits, respectively (out of a total of 307; activity defined as average residual activity of less than 80%). The conclusions are 2-fold. On one hand, the exact fragment-matching similarity searching method employed here is not capable of finding completely novel hit structures. On the other hand, this study emphasizes the requirement for a comparable distribution of chemical features of the training and test sets. MOLPRINT 2D is freely downloadable from http://www.cheminformatics.org.

Bayes Theorem↗

PubMed2004

Drug permeation in biomembranes: in vitro and in silico prediction and influence of physicochemical properties.

In the past decades, it has become increasingly apparent that in addition to therapeutic effect, drugs need to exhibit favourable absorption, distribution, metabolism and excretion (ADME) characteristics to produce a desirable response in vivo. As the recent progress in drug discovery technology enables rapid synthesis of vast numbers of potential drug candidates, robust methods are required for the effective screening of compounds synthesized within such programs, so that compounds with poor pharmacokinetic properties can be rejected at an early stage of drug development. Furthermore, a viable in silico method would save resources by enabling virtual screening of drug candidates already prior to synthesis. This review gives a general overview of the approaches aimed at predicting biological permeation, one of the cornerstones behind the ADME behaviour of drugs. The most important experimental and computational models are reviewed. Physicochemical factors underlying the permeation process are discussed.

Algorithms↗

PubMed2005

Components of successful lead generation.

This review aims to give an overview of current good practice in the prosecution of Lead Generation. It will assess experiences across the field as judged from the contents of the limited number of peer-review disclosures to date. It will also rely heavily on the experiences of the authors from many campaigns within this organisation. Its focus will be on the assembly of an appropriate compound collection for application in High Throughput Screening (HTS), the prosecution of HTS, the profiling of HTS output and, lastly the Hit-to-Lead optimisation of selected HTS output. Excluded from the scope are detailed aspects of library design [1], parallel synthesis [2], virtual library applications [3], virtual screening [4] and fragment screening [5] approaches, all of which have been the subject of recent reviews.

Animals↗

PubMed2005

Multiple target screening method for robust and accurate in silico ligand screening.

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].

Databases, Factual↗

PubMed1999

High Throughput Screening for Drug Discovery: Continually Transitioning into New Technology.

Those working in HTS laboratories, pressured to find increasing numbers of drug leads while containing costs, are seeking larger compound sets, more automated systems to screen them faster, and an integrated set of equipment and consumables. Enabling technologies are continually being developed and suppliers are teaming up to supply integrated equipment and consumable sets. Miniaturization, microfluidic chips, subnanoliter dispensing, fluorescence, homogeneous assays for HTS, and virtual screening are just some of the evolving tools that HTS experts are continually evaluating and incorporating into drug discovery operations.

Journal Article↗

PubMedDate not supplied

Efficient 3D database screening for novel HIV-1 IN inhibitors.

We describe the use of pharmacophore modeling as an efficient tool in the discovery of novel HIV-1 integrase (IN) inhibitors. A three-dimensional hypothetical model for the binding of diketo acid analogues to the enzyme was built by means of the Catalyst program. Using these models as a query for virtual screening, we found several compounds that contain the specified 3D patterns of chemical functions. Biological testing shows that our strategy was successful in searching for new structural leads as HIV-1 IN inhibitors.

Computer Simulation↗

PubMed2005

Non-stochastic and stochastic linear indices of the molecular pseudograph's atom-adjacency matrix: a novel approach for computational in silico screening and "rational" selection of new lead antibacterial agents.

A novel approach (TOMOCOMD-CARDD) to computer-aided "rational" drug design is illustrated. This approach is based on the calculation of the non-stochastic and stochastic linear indices of the molecular pseudograph's atom-adjacency matrix representing molecular structures. These TOMOCOMD-CARDD descriptors are introduced for the computational (virtual) screening and "rational" selection of new lead antibacterial agents using linear discrimination analysis. The two structure-based antibacterial-activity classification models, including non-stochastic and stochastic indices, classify correctly 91.61% and 90.75%, respectively, of 1525 chemicals in training sets. These models show high Matthews correlation coefficients (MCC=0.84 and 0.82). An external validation process was carried out to assess the robustness and predictive power of the model obtained. These QSAR models permit the correct classification of 91.49% and 89.31% of 505 compounds in an external test set, yielding MCCs of 0.84 and 0.79, respectively. The TOMOCOMD-CARDD approach compares satisfactorily with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, an in silico screening of 87 new chemicals reported in the anti-infective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new lead antibacterial compounds.

Algorithms↗

PubMed2006

Grid-enabled high-throughput in silico screening against influenza A neuraminidase.

Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM), the second data challenge battling avian flu was kicked off in April 2006 to identify new drugs for the potential variants of the influenza A virus. Mobilizing thousands of CPUs on the Grid, the six-week-long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a worldwide Grid infrastructure to efficiently deploy large-scale virtual screening to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.

Binding Sites↗