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PubMed2002

Development of biologically active compounds by combining 3D QSAR and structure-based design methods.

One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved--namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data.

Binding Sites↗
PubMed2005

Virtual ligand screening against Escherichia coli dihydrofolate reductase: improving docking enrichment using physics-based methods.

Motivated by their participation in the McMaster Data-Mining and Docking Competition, the authors developed 2 new computational technologies and applied them to docking against Escherichia coli dihydrofolate reductase: a receptor preparation procedure that incorporates rotamer optimization of side chains and a physics-based rescoring procedure for estimating relative binding affinities of the protein-ligand complexes. Both methods use the same energy function, consisting of the all-atom OPLS-AA force field and a generalized Born solvent model, which treats the protein receptor and small-molecule ligands in a consistent manner. Thus, the energy function is similar to that used in more sophisticated approaches, such as free-energy perturbation and the molecular mechanics Poisson-Boltzmann/surface area, but sampling during the rescoring procedure is limited to simple energy minimization of the ligand. The use of a highly efficient minimization algorithm permitted the authors to apply this rescoring procedure to hundreds of thousands of protein-ligand complexes during the competition, using a modest Linux cluster. To test these methods, they used the 12 competitive inhibitors identified in the training set, plus methotrexate, as positive controls in enrichment studies with both the training and test sets, each containing 50,000 compounds. The key conclusion is that combining the receptor preparation and rescoring methods makes it possible to identify most of the positive controls within the top few tenths of a percent of the rank-ordered training and test set libraries.

Computational Biology↗
PubMed2004

New lead generation strategies for protein kinase inhibitors - fragment based screening approaches.

The protein kinase superfamily represents both an enormous opportunity and a unique challenge for drug discovery. Protein kinases play central roles in the cellular economy and it is well known that a large number of diseases involve aberrant protein kinase activity. This review discusses how fragment based screening strategies, such as virtual screening, NMR and high-throughput X-ray crystallography are being employed to identify new chemo-types to produce the next generation of protein kinase inhibitors.

Animals↗
PubMed2005

A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators.

We developed a pharmacophore-based evolutionary approach for virtual screening. This tool, termed the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach with a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to expedite convergence. The latter, integrating an empirical-based energy function and pharmacological preferences (binding-site pharmacological interactions and ligand preferences), simultaneously serves as the scoring function for both molecular docking and postdocking analyses to improve screening accuracy. We apply pharmacological interaction preferences to select the ligands that form pharmacological interactions with target proteins, and use the ligand preferences to eliminate the ligands that violate the electrostatic or hydrophilic constraints. We assessed the accuracy of our approach using human estrogen receptor (ER) and a ligand database from the comparative studies of Bissantz et al. (J Med Chem 2000;43:4759-4767). Using GEMDOCK, the average goodness-of-hit (GH) score was 0.83 and the average false-positive rate was 0.13% for ER antagonists, and the average GH score was 0.48 and the average false-positive rate was 0.75% for ER agonists. The performance of GEMDOCK was superior to competing methods such as GOLD and DOCK. We found that our pharmacophore-based scoring function indeed was able to reduce the number of false positives; moreover, the resulting pharmacological interactions at the binding site, as well as ligand preferences, were important to the screening accuracy of our experiments. These results suggest that GEMDOCK constitutes a robust tool for virtual database screening.

Binding Sites↗
PubMed2005

Consensus on current clinical practice of virtual colonoscopy.

OBJECTIVE: The purpose of our study was to determine the current opinions regarding the performance, interpretation, reporting, and clinical role of virtual colonoscopy among a group of selected experts to develop a consensus statement. MATERIALS AND METHODS: A questionnaire was sent to 33 selected experts in virtual colonoscopy. Responses were tabulated and results were used to develop a consensus statement. The results of the questionnaire and consensus statement were sent to respondents for comment and approval. RESULTS: Thirty-one (93.9%) of 33 surveys were returned. Eighty-seven percent (27/31) of respondents believe virtual colonoscopy is a credible screening method. Oral sodium phosphate solution is the laxative preferred by more than 66% (18/27), whereas 62% (13/21) do not believe fecal tagging is necessary. All respondents (25/25) think that both prone and supine imaging is required, with most (81%, 21/26) believing IV contrast material is not necessary. The routine use of spasmolytics is suggested by only 15% (4/26). The largest acceptable slice thickness of 3 mm is agreed on by 88% (22/25). All respondents believe screening virtual colonoscopy should be performed at a lower dose per slice than conventional CT. Most (80%, 20/25) believe the optimum method of interpreting virtual colonoscopy should be primary axial review, with 3D used for problem solving. All but one respondent (96%, 26/27) agree there is a threshold size below which polyps are not clinically important. When reporting virtual colonoscopy results, 59% (16/27) believe polyps less than 4 mm need not be reported. CONCLUSION: A consensus is developing among experts as to the appropriate manner in which virtual colonoscopy should be performed, interpreted, and reported.

Colonography, Computed Tomographic↗
PubMed2000

A neural network based virtual high throughput screening test for the prediction of CNS activity.

A virtual high throughput screening test to identify potentially CNS-active drugs has been developed. Discrimination was based on the knowledge available in databases containing CNS-active (Cipsline from Prous Science) and inactive compounds (Chemical Directory from Sigma-Aldrich). Molecular structures were represented using 2D Unit y fingerprints and a feedforward neural network was trained to classify molecules regarding their CNS activity. The parameterized network was validated by reclassification of the training set elements, by the classification of a test set preselected from the Prous database, and also by the prediction of activity for known CNS drugs not used in the training set but available in the Medchem database (Daylight). These tests revealed that our neural net recognized at least 89% of CNS-active compounds and would be suitable for use in our virtual screening protocol.

Artificial Intelligence↗
PubMed2006

Chemogenomics approaches to G-protein coupled receptor lead finding.

G-protein coupled receptors (GPCRs) are promising targets for the discovery of novel drugs. In order to identify novel chemical series, high-throughput screening (HTS) is often complemented by rational chemogenomics lead finding approaches. We have compiled a GPCR directed screening set by ligand-based virtual screening of our corporate compound database. This set of compounds is supplemented with novel libraries synthesized around proprietary scaffolds. These target-directed libraries are designed using the knowledge of privileged fragments and pharmacophores to address specific GPCR subfamilies (e.g., purinergic or chemokine-binding GPCRs). Experimental testing of the GPCR collection has provided novel chemical series for several GPCR targets including the adenosine A1, the P2Y12, and the chemokine CCR1 receptor. In addition, GPCR sequence motifs linked to the recognition of GPCR ligands (termed chemoprints) are identified using homology modeling, molecular docking, and experimental profiling. These chemoprints can support the design and synthesis of compound libraries tailor-made for a novel GPCR target.

Animals↗
PubMed2005

Height effects in real and virtual environments.

The study compared human perceptions of height, danger, and anxiety, as well as skin conductance and heart rate responses and postural instability effects, in real and virtual height environments. The 24 participants (12 men, 12 women), whose average age was 23.6 years, performed "lean-over-the-railing" and standing tasks on real and comparable virtual balconies, using a surround-screen virtual reality (SSVR) system. The results indicate that the virtual display of elevation provided realistic perceptual experience and induced some physiological responses and postural instability effects comparable to those found in a real environment. It appears that a simulation of elevated work environment in a SSVR system, although with reduced visual fidelity, is a valid tool for safety research. Potential applications of this study include the design of virtual environments that will help in safe evaluation of human performance at elevation, identification of risk factors leading to fall incidents, and assessment of new fall prevention strategies.

Accidental Falls↗
PubMed2005

Virtual microscopy:applications to hematology.

Virtual microscopy is the simulation of microscopy over a computer network. A virtual slide is a giant digital image file of a glass slide that can be displayed, panned, zoomed, and focused in a virtual slide viewer on a computer screen. Virtual slides represent a revolutionary advance over glass slides. They are easy to file, store, retrieve, annotate, and mark and can be preserved indefinitely. Furthermore, they are easy to duplicate and distribute and can be integrated into electronic patient records. Large virtual slides can be readily transmitted to users over a standard broadband connection. With the recent introduction of viewers that can focus virtual slides, virtual microscopy can simulate all the functions of real microscopy. Virtual microscopy has significant advantages over real microscopy in education and in proficiency testing. In education, virtual microscopy enables "anytime, anywhere" learning and has been favorably received by students and teachers. In proficiency surveys, all users view the same image, virtual slides are easy to distribute, and the slides do not deteriorate. Potential applications for hematology proficiency surveys include blood and bone marrow morphology, differential cell counts, cytochemistry and immunocytochemistry, detection of malarial parasites, and other tests. Virtual microscopy enables proficiency surveys of critical clinical parameters, such as the bone marrow blast count, and implementation of "locate and identify" exercises. It is conceivable that with the next generation of technological developments, virtual microscopy can be extended to diagnostic applications. Important goals are to minimize slide file size without loss of relevant detail, to establish diagnostic equivalence, and to automate virtual slide capture with high throughput for integration into laboratory information systems. Key factors that will drive implementation include user-friendliness, cost, data storage requirements, and throughput speed. Implementation may have constructive effects on teaching and learning, the peer-to-peer consultative process, and diagnostic accuracy and performance.

Hematology↗
PubMed2003

How reliable is the evidence for screening mammography?

Substantial reduction in breast cancer mortality has been proven in randomized trials of screening mammography. These trials have found a statistically significant benefit both for women ages 40-49 years and for women age 50 years and older at onset of screening. In fact, it is likely that the trials actually underestimate the benefit for an individual woman who is screened. Service screening programs have shown a 63% reduction in deaths from breast cancer among women who are screened. Virtually all major medical organizations now advise screening mammography for women ages 40 and older. Two recent studies that questioned the validity of results from screening mammography trials are themselves fatally flawed.

Adult↗
PubMed2025

Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space.

Significant efforts have been recently dedicated to the discovery of small molecule inhibitors against the Macrodomain 1 (Mac1) of nonstructural protein 3 (NSP3) as potential antivirals for SARS-CoV-2. Thus, Mac1 has also been selected as the target for the Critical Assessment of Hit-finding Experiments (CACHE) challenge #3. As contestants in that challenge, we developed a computational strategy that ranked on the top among all 23 participants in the competition and resulted in the discovery of a novel chemical series of non-charged Mac1 inhibitors. Those have been identified through the combination of machine learning-accelerated virtual screening of Enamine REAL Diversity Subset of approximately 25 million compounds and consequent hit expansion into the entire Enamine REAL Space library. In particular, the initially identified hit compound CACHE3-HI_1706_56 (KD = 20 μM) was explored by probing 17 close analogues from a library of 44 billion molecules from the Enamine REAL. All those analogues effectively displaced the Mac1-binding ADP-ribose peptide, and 12 were confirmed to engage with Mac1 by the Surface Plasmon Resonance experiments, revealing a new chemical series of compounds for hit-to-lead optimization. The structure of the CACHE3-HI_1706_56-Mac1 complex was further determined at high resolution with crystallography, confirming initial computational predictions. Our results illustrate the effectiveness of ML-accelerated docking to rapidly identify novel chemical series and provide a strong foundation for the development of SARS-CoV-2 NSP3 Mac1 inhibitors.

CACHE challenge↗
PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗
PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗
PubMed2026

Structure-based drug design of small-molecule c-Myc G-quadruplex binders.

The c-Myc oncogene is crucial in tumorigenesis. Although it is a promising therapeutic target, its protein lacks a conventional drug-binding pocket, making it traditionally "undruggable". Recent studies show that the c-Myc promoter can form a G-quadruplex (G4) structure, which suppresses transcription and offers a new strategy for indirect inhibition. In this study, structure-based virtual screening was performed using the c-Myc G4 crystal structure to screen the ChemDiv compound library, aiming to identify small molecules that bind to the G4 structure. Candidate compounds were evaluated in preliminary in vitro assays for biological activity. The results showed that Y502-3888 binds to the c-Myc G4 and downregulates c-Myc expression at both mRNA and protein levels. Collectively, these findings support the potential of Y502-3888 as a c-Myc G4 binder for the treatment of multiple myeloma (MM), providing a foundation for future development of anticancer agents targeting the c-Myc G4.

G-Quadruplexes↗
PubMed2026

Structure-guided discovery of non-catechol dopamine D1 receptor ligands with biased agonism and antagonism.

The catechol L-DOPA, a cornerstone of Parkinson's disease (PD) treatment, has two major drawbacks: poor pharmacokinetics and, more significantly, debilitating dyskinesias from chronic dopamine D1 receptor (D1R) activation. Preclinical rodent studies suggest that D1R antagonism or β-arrestin-biased agonism can alleviate these motor complications, highlighting the need for next-generation non-catechol ligands. Through virtual screening, we identified eight novel chemotypes as D1R ligands, including two G protein-biased agonists, two β-arrestin-biased agonists and four antagonists. Structure-activity relationship (SAR) optimization led to the development of A82R, a non-catechol D1R antagonist (Ki 733 nM) with high D1 family over D2 family selectivity. Additionally, we present A69, a novel non-catechol β-arrestin-biased partial agonist for D1R (Ki 86.9 nM, stronger than representative D1R commercial drugs) with a sustained half-life of 1 h in the mouse brain. We show that the observed selectivity patterns are consistent with structural and information-theoretic limits on dopamine's ability to encode receptor subtype identity. Within these bounds, the non-catechol ligand chemotypes represent promising leads for developing therapies that modulate D1R signaling and reduce L-DOPA-induced dyskinesia in PD.

Receptors, Dopamine D1↗
PubMed2005

The devil is still in the details--driving early drug discovery forward with biophysical experimental methods.

This review comments on some recent trends and insights in the field of lead identification and optimization with a bias toward the increased use of biophysical methods, particularly in combination with three-dimensional structural information. While high-throughput screening, combinatorial chemistry and, most recently, in silico virtual screening techniques have made well-resourced but only partially successful attempts to meet the challenge of identifying new drug candidates by playing 'the large numbers game', another group of technologies are now approaching the same challenge from what might be considered the opposite extreme. The common strategy of these technologies is to focus on a smaller set of low-molecular-weight compounds whose interactions with a target are characterized with the aid of sensitive assays, most often high-quality biophysical techniques such as biosensors, calorimetry, nuclear magnetic resonance spectroscopy and X-ray crystallography. The advantages of such an approach include more optimal and chemically attractive starting points, immediate access to reliable measurements of binding properties, the mapping of ligand interactions on the atomic level and, most importantly, a greater control of experimental errors at the initial stages of drug discovery where compounds are either discovered or lost. When correctly supported, this more careful approach appears to deliver quality leads, even for the so-called 'difficult' targets. As these techniques are complementary to traditional methods, companies should be less hesitant to invest in them. The biophysical methods that are used to drive this approach have made something of a return to drug discovery after having been discarded for being too slow, too expensive or too old-fashioned by the over-optimistic supporters of high-throughput and statistical/computational in silico methods.

Animals↗
PubMed2001

Extracolonic findings at virtual colonoscopy: implications for screening programs.

OBJECTIVE: Virtual colonoscopy (VC) is an evolving technology proposed as a possible screening tool for colorectal cancer. In contrast to conventional colonoscopy, VC may detect extracolonic abdominal pathology. This may lead to unnecessary investigation of benign lesions, or may benefit the patient by identifying serious pathology at an early stage. The aim of this study was to assess the prevalence and characteristics of extracolonic pathology found in patients undergoing VC. METHODS: A total of 100 patients aged > or = 55 yr, referred for colonoscopy for bowel symptoms or family history of bowel cancer, underwent VC. Axial views of the abdomen were reviewed prospectively by a single radiologist for extracolonic pathology. Patients with extracolonic abnormalities were referred to their local doctor or to a specialist clinic when appropriate. Case records were reviewed and treating doctors contacted to document subsequent investigations and procedures generated. RESULTS: Fifteen patients (15%) had extracolonic abnormalities detected. In four patients, the pathology had been diagnosed previously (umbilical hernia, gallbladder and renal calculi, 3.5-cm aortic aneurysm, ovarian cyst). Eleven patients had new abnormalities detected: ovarian cysts (three), liver cysts (two), uterine fibroids (two), gallstones (one), splenic calcifications (one), aortic aneurysm (one), and renal tumor (one). Two patients with ovarian cysts underwent surgery, and histology showed benign cysts. CONCLUSIONS: Extracolonic abnormalities are common at VC. Most are benign, but may lead to investigative and procedural costs. These data should be carefully evaluated in feasibility and cost-effectiveness studies on colorectal cancer screening using VC.

Aged↗