Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

Near-Infrared Fluorescence Probe for Monoamine Oxidase A with a Large Stokes Shift for Intraoperative Navigation

Monoamine oxidase A (MAO-A) is a dimeric flavoprotein that is found in the mitochondrial membrane. Currently, there is a lack of near-infrared fluorescent probes (NIR-FPs) with good specificity and high sensitivity for detecting MAO-A, making it difficult to accurately recognize and image cells in vitro and in vivo. In this study, the NIR-FP DDM-NH2 was designed and synthesized in order to detect MAO-A specifically in live biological systems. The probe comprised two functional components: dicyanoisophosphone as an NIR dye precursor and alanine as a recognition moiety. After identifying MAO-A, the probe exhibited an NIR emission peak at 770 nm with a significant Stokes shift (180 nm), 11-fold response factor, low detection limit of 99.7 nM, and considerably higher affinity toward MAO-A than that toward MAO-B, indicating high sensitivity. In addition, DDM-NH2 was effective when applied to the image-based assessment of MAO-A activity in HeLa cells, zebrafish, and tumor-bearing mice, demonstrating great potential for visualization-based research and MAO-A application in vivo.

In vitro and in vivo Biological Evaluation of Newly Tacrine-Selegiline Hybrids as Multi-Target Inhibitors of Cholinesterases and Monoamine Oxidases for Alzheimer's Disease

Purpose

Alzheimer's disease (AD) is the most common neurodegenerative disease, and its multifactorial nature increases the difficulty of medical research. To explore an effective treatment for AD, a series of novel tacrine-selegiline hybrids with ChEs and MAOs inhibitory activities were designed and synthesized as multifunctional drugs.

Methods

All designed compounds were evaluated in vitro for their inhibition of cholinesterases (AChE/BuChE) and monoamine oxidases (MAO-A/B) along with their blood-brain barrier permeability. Then, further biological activities of the optimizing compound 7d were determined, including molecular model analysis, in vitro cytotoxicity, acute toxicity studies in vivo, and pharmacokinetic and pharmacodynamic property studies in vivo.

Results

Most synthesized compounds demonstrated potent inhibitory activity against ChEs/MAOs. Particularly, compound 7d exhibited good and well-balanced activity against ChEs (hAChE: IC50 = 1.57 μM, hBuChE: IC50 = 0.43 μM) and MAOs (hMAO-A: IC50 = 2.30 μM, hMAO-B: IC50 = 4.75 μM). Molecular modeling analysis demonstrated that 7d could interact simultaneously with both the catalytic active site (CAS) and peripheral anionic site (PAS) of AChE in a mixed-type manner and also exhibits binding affinity towards BuChE and MAO-B. Additionally, 7d displayed excellent permeability of the blood-brain barrier, and under the experimental conditions, it elicited low or no toxicity toward PC12 and BV-2 cells. Furthermore, 7d was not acutely toxic in mice at doses up to 2500 mg/kg and could improve the cognitive function of mice with scopolamine-induced memory impairment. Lastly, 7d possessed well pharmacokinetic characteristics.

Conclusion

In light of these results, it is clear that 7d could potentially serve as a promising multi-functional drug for the treatment of AD.

Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer

BACKGROUND/OBJECTIVES

Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4.

SUBJECTS/METHODS

In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays.

RESULTS

The primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4.

CONCLUSIONS

Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.

Synthesis and Molecular Docking of New 1,2,3-triazole Carbohydrates with COVID-19 Proteins

AIMS

We have established this paper to recommend a novel way for the preparation of carbohydrates encompassing a 1,2,3-triazole motif that was prepared using an efficient click chemistry synthesis.

BACKGROUND

The SARS-CoV-2 coronavirus epidemic continues to spread at a fast rate worldwide. The main protease (Mpro) is useful target for anti-COVID-19 agents. Triazoles are frequently found in many bioactive products, such as coronavirus inhibitors.

OBJECTIVE

Click reactions are facilitated via the activation of copper nanoparticles, different substrates have been tested using this adopted procedure given in all cases, in high yields and purity. Other interesting comparative docking analyses will be the focus of this article. Calculations of quantitative structure-activity relationships will be studied.

METHODS

Copper nanoparticles were produced by the reaction of cupric acetate monohydrate with oleylamine and oleic acid. To a solution, 5-(azidomethyl)-2,2,7,7-tetramethyltetrahydro-5Hbis([ 1,3]dioxolo)[4,5-b:4',5'-d]pyran 2 (200 mg, 0.72 mmol, 1 eq.) in toluene (15 mL) was added into a mixture of N-(prop-2-yn-1-yl)benzamide derivatives 1a-d (1.5 eq.) and copper nanoparticles (0.57 mg, 0.036 mmol, 0.05 eq.).

RESULTS

A novel series of 1,2,3-triazole carbohydrate skeletons were modeled and efficiently synthesized. Based on the observations, virtual screening using molecular docking was performed to identify novel compounds that can bind with the protein structures of COVID-19 (PDB ID: 6LU7 and 6W41). We believed that the 1,2,3-triazole carbohydrate derivatives could aid in COVID-19 drug discovery.

CONCLUSION

The formations of targeted triazoles were confirmed by different spectroscopic techniques (FT-IR, 1H NMR, 13C NMR, and CHN analyses). The docking scores of the newly synthesized triazole are attributed to the presence of hydrogen bonds together with many interactions between the ligands and the active amino acid residue of the receptor. The comparison of the interactions of the drugs, remdesivir and triazole, in the largest pocket of 6W41 and 6LU7 is also presented.

Identification of new small molecule monoamine oxidase-B inhibitors through pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation studies

The discovery of a safe and efficacious drug is a complex, time-consuming, and expensive process. Computational methodologies driven by cheminformatics tools play a central role in the high-throughput lead discovery and optimization process especially when the structure of the biological target is known. Monoamine oxidases are the membrane-bound FAD-containing enzymes and the isoform monoamine oxidase-B (MAO-B) is an attractive target for treating diseases like Alzheimer's disease, Parkinson's disease, glioma, etc. In the current study, we have used a pharmacophore-based virtual screening technique for the identification of new small molecule MAO-B inhibitors. Safinamide was used for building a pharmacophore model and the developed model was used to probe the ZINC database for potential hits. The obtained hits were filtered against drug-likeness and PAINS. Out of the hit's library, two compounds ZINC02181408, ZINC08853942 (most active), and ZINC53327382 (least active) were further subjected to molecular docking and dynamics simulation studies to assess their virtual binding affinities and stability of the resultant protein-ligand complex. The docking studies revealed that active ligands were well accommodated within the active site of MAO-B and interacted with both substrate and entrance cavity residues. MD simulation studies unveiled additional hydrogen bond interactions with the substrate cavity residues, Tyr398 and Tyr435 that are crucial for the catalytic role of MAO-B. Moreover, the predicted ADMET parameters suggest that the compounds ZINC08853942 and ZINC02181408 are suitable for CNS penetration. Thus, the attempted computational campaign yielded two potential MAO-B inhibitors that merit further experimental investigation.Communicated by Ramaswamy H. Sarma.

Almond intake during pregnancy in rats improved the cognitive performance of adult male offspring

Background: Based on evidence there are accepted links among early nutrition, epigenetic processes, and cognitive performance. Almond as a nutritious food could exert neuroprotective effects and improve anxiety, learning, and memory.Methods: In the current study, female rats were fed with a diet containing 5% (w/w) almonds during the mating period (two days) and gestation period (21 consecutive days). Then, the effect of the almond diet on short-term memory (Y maze), anxiety (elevated plus maze), and stress adaptation (forced swimming test) were investigated in the adult male offspring. The hippocampus (HIP), prefrontal cortex (PFC), and amygdala (AMY) of offspring were collected, and the level of cyclic AMP response element-binding proteins (CREB), brain-derived neurotrophic factor (BDNF) was assessed by western blotting. Also, Monoamine oxidases (MAO)-A and B activity were evaluated via enzymatic assays.Results: Our results indicated that prenatal almond consumption improved memory, made a modest reduction in anxiety-like behavior, and increased stress adaptation in adult male offspring. Also, molecular assessments showed an increased level of CREB phosphorylation and BDNF in the HIP and PFC of the almond group, while the activity of MAO-A and MAO-B was inhibited by almond consumption in mentioned areas.Discussion: These findings introduce almonds as a beneficial diet during pregnancy, for improving short-term memory, stress adaptation, and cognitive performance in adult offspring.

WaterMap guided structure-based virtual screening for acetylcholinesterase inhibitors

Structure-based virtual screening of the Enamine database of 1.7 million compounds followed by WaterMap calculations (a molecular dynamics simulation-based method) was applied to identify novel AChE inhibitors. The inhibitory potency of 29 selected compounds against electric eel (ee) AChE was determined using the Ellman's method. Three compounds were found active (success rate 10%). For the most potent compound (~40% of inhibition at 10 μM), 20 derivatives were discovered based on the Enamine similarity search. Finally, five compounds were found promising  (IC 50 ranged from 6.3 µM to 17.5 µM) inhibitors of AChE. The performed similarity and fragment analysis confirmed significant structural novelty of novel AChE inhibitors. Toxicity/safety of selected compounds was determined in zebrafish model.

Simultaneously Detecting Monoamine Oxidase A and B in Disease Cell/Tissue Samples Using Paper-Based Devices

As enzymes in the outer membrane of the mitochondrion, monoamine oxidases (MAOs) can catalyze the oxidative deamination of monoamines in the human body. According to different substrates, MAOs can be divided into MAO-A and MAO-B. The imbalance of the MAO-A is associated with neurological degeneration, while excess MAO-B activity is closely connected with Parkinson's disease (PD) and Alzheimer's disease (AD); therefore, detection of MAOs is of great significance for the diagnosis and treatment of these diseases. This work reports the multiplexed detection of MAO-A and MAO-B using paper-based devices based on chemiluminescence (CL). The detection limits were 5.01 pg/mL for MAO-A and 8.50 pg/mL for MAO-B in human serum. In addition, we used paper-based devices to detect MAOs in human cells and tissue samples and found that the results of paper-based detection and Western blotting (WB) showed the same trend. While only one antibody can be incubated on the same membrane by WB, multiple antibodies incubated on the same paper enabled simultaneous detection of MAO-A and MAO-B by paper-based devices. The paper-based assay could be used for preliminary early screening of clinical samples for MAOs and can be extended as an alternative to WB for multiplexed detection of various proteins in disease cell or tissue samples.

Single Heterocyclic Compounds as Monoamine Oxidase Inhibitors: From Past to Present

Inhibitors of monoamine oxidase (MAO) have shown therapeutic values in a variety of neurodegenerative diseases such as depression, Parkinson's disease and Alzheimer's disease. Heterocyclic compounds exhibit a broad spectrum of biological activities and vital leading compounds for the development of chemical drugs. Herein, we focus on the synthesis and screening of novel single heterocyclic derivatives with MAO inhibitory activities during the past decade. This review covers recent pharmacological advancements of single heterocyclic moiety along with structure- activity relationship to provide better correlation among different structures and their receptor interactions.

Discovery of Novel Neuraminidase Inhibitors by Structure-Based Virtual Screening, Structural Optimization, and Bioassay

Neuraminidase (NA) is a significant therapeutic target for treating influenza. In this study, a new lead NA inhibitor AN-329/10738021 was discovered by structure-based virtual screening, molecular dynamics simulations, and bioassay validation. Optimization of lead AN-329/10738021, which holds a novel scaffold of N'-benzylidene benzohydrazone, leads to discovery of some novel NA inhibitors Y-1-Y-11. Compound Y-1 exerts the best inhibition activity (IC₅₀ = 0.21 μM) against NA, which is better than oseltamivir carboxylate (OSC) (IC₅₀ = 3.04 μM) and lead AN-329/10738021 (IC₅₀ = 1.92 μM). Molecular docking analysis indicates that the good potency of Y-1 may be ascribed to the elongation of the benzylidene moiety of the molecule to the 430-cavity. The results of this study may offer useful reference for development of novel NA inhibitors.

Quercetin‑3‑O‑α‑L‑rhamnopyranoside derived from the leaves of Lindera aggregata (Sims) Kosterm. evokes the autophagy‑induced nuclear factor erythroid 2‑related factor 2 antioxidant pathway in human umbilical vein endothelial cells

Quercetin‑3‑O‑α‑L‑rhamnopyranoside (QI) is derived from the leaves of Lindera aggregata (Sims) Kosterm. And exhibits multiple biological activities, including an antioxidant activity. However, the detailed molecular mechanism of its antioxidant activity remains unknown. The aim of the present study was to investigate the antioxidant activity of QI and the underlying molecular mechanism in human umbilical vein endothelial cells (HUVECs). An oxidative stress model was established in HUVECs using H2O2, and cells were then treated with different concentrations of QI. The results revealed that the exposure of HUVECs to QI protected these cells from H2O2‑induced damage. QI treatment also increased the activities of the antioxidant enzymes superoxide dismutase (SOD) and glutathione (GSH) in the cell culture medium. In addition, QI inhibited H2O2‑induced apoptosis by decreasing the expression levels of cleaved Caspase‑9 and poly(ADP‑ribose) polymerase. QI also inhibited the production of DNA fragments and reactive oxygen species induced by H2O2. Furthermore, QI decreased the oxidative stress by promoting the nuclear transfer of nuclear factor erythroid 2‑related factor 2 (Nrf2) and heme oxygenase‑1 by activating autophagy, and inhibited the competition of Bach1 from Nrf2. Finally, QI significantly improved the activities of T‑SOD and GSH, and decreased the content of malondialdehyde in the serum and heart tissue of aging rats. These data support the use of QI as a health supplement to alleviate oxidative stress or further development of this compound as an antioxidant drug.

Molecular modeling of Plasmodium falciparum peptide deformylase and structure-based pharmacophore screening for inhibitors

The role of metal coordination geometry and actinonin (inhibitor) binding was examined to develop pharmacophore-based inhibitor design strategy forPlasmodium falciparumpeptide deformylase.

Molecular dynamics-based discovery of novel phosphodiesterase-9A inhibitors with non-pyrazolopyrimidinone scaffolds

Phosphodiesterase-9A (PDE9A) is a promising therapeutic target for the treatment of diabetes and Alzheimer's disease (AD). The Pfizer PDE9A inhibitor PF-04447943 has completed Phase II clinical trials in subjects with mild to moderate AD in 2013. However, most of the reported PDE9A inhibitors share the same scaffold as pyrazolopyrimidinone, which lacks structural diversity and is unfavorable for the development of novel PDE9A inhibitors. In the present study, a combinatorial method including pharmacophores, molecular docking, molecular dynamics simulations, binding free energy calculations, and bioassay was used to discover novel PDE9A inhibitors with new scaffolds rather than pyrazolopyrimidinones from the SPECS database containing about 200,000 compounds. As a result, 15 hits out of 29 molecules (a hit rate of 52%) with five novel scaffolds were identified to be PDE9A inhibitors with inhibitory affinities no more than 50 μM to enrich the structural diversity, different from the pyrazolopyrimidinone-derived family. The high hit ratio of 52% for this virtual screening method indicated that the combinatorial method is a good compromise between computational cost and accuracy. Binding pattern analyses indicate that those hits with non-pyrazolopyrimidinone scaffolds can bind the same active site pocket of PDE9A as classical PDE9A inhibitors. In addition, structural modification of compound AG-690/40135604 (IC50=8.0 μM) led to a new one, 16, with an improved inhibitory affinity of 2.1 μM as expected. The five novel scaffolds discovered in the present study can be used for the rational design of PDE9A inhibitors with higher affinities.

Novel design strategy for checkpoint kinase 2 inhibitors using pharmacophore modeling, combinatorial fusion, and virtual screening

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the Best(train)Best(test) and Fast(train)Fast(test) prediction results. The potential inhibitors were selected from NCI database by screening according to Best(train)Best(test) + Fast(train)Fast(test) prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study.

Development of a human dihydroorotate dehydrogenase (hDHODH) pharma-similarity index approach with scaffold-hopping strategy for the design of novel potential inhibitors

Human dihydroorotate dehydrogenase (hDHODH) is a class-2 dihydroorotate dehydrogenase. Because it is extensively used by proliferating cells, its inhibition in autoimmune and inflammatory diseases, cancers, and multiple sclerosis is of substantial clinical importance. In this study, we had two aims. The first was to develop an hDHODH pharma-similarity index approach (PhSIA) using integrated molecular dynamics calculations, pharmacophore hypothesis, and comparative molecular similarity index analysis (CoMSIA) contour information techniques. The approach, for the discovery and design of novel inhibitors, was based on 25 diverse known hDHODH inhibitors. Three statistical methods were used to verify the performance of hDHODH PhSIA. Fischer’s cross-validation test provided a 98% confidence level and the goodness of hit (GH) test score was 0.61. The q², r², and predictive r² values were 0.55, 0.97, and 0.92, respectively, for a partial least squares validation method. In our approach, each diverse inhibitor structure could easily be aligned with contour information, and common substructures were unnecessary. For our second aim, we used the proposed approach to design 13 novel hDHODH inhibitors using a scaffold-hopping strategy. Chemical features of the approach were divided into two groups, and the Vitas-M Laboratory fragment was used to create de novo inhibitors. This approach provides a useful tool for the discovery and design of potential inhibitors of hDHODH, and does not require docking analysis; thus, our method can assist medicinal chemists in their efforts to identify novel inhibitors.

Design checkpoint kinase 2 inhibitors by pharmacophore modeling and virtual screening techniques

Damage to DNA is caused by ionizing radiation, genotoxic chemicals or collapsed replication forks. When DNA is damaged or cells fail to respond, a mutation that is associated with breast or ovarian cancer may occur. Mammalian cells control and stabilize the genome using a cell cycle checkpoint to prevent damage to DNA or to repair damaged DNA. Checkpoint kinase 2 (Chk2) is one of the important kinases, which strongly affects DNA-damage and plays an important role in the response to the breakage of DNA double-strands and related lesions. Therefore, this study concerns Chk2. Its purpose is to find potential inhibitors using the pharmacophore hypotheses (PhModels) and virtual screening techniques. PhModels can identify inhibitors with high biological activities and virtual screening techniques are used to screen the database of the National Cancer Institute (NCI) to retrieve compounds that exhibit all of the pharmacophoric features of potential inhibitors with high interaction energy. Ten PhModels were generated using the HypoGen best algorithm. The established PhModel, Hypo01, was evaluated by performing a cost function analysis of its correlation coefficient (r), root mean square deviation (RMSD), cost difference, and configuration cost, with the values 0.955, 1.28, 192.51, and 16.07, respectively. The result of Fischer's cross-validation test for the Hypo01 model yielded a 95% confidence level, and the correlation coefficient of the testing set (rtest) had a best value of 0.81. The potential inhibitors were then chosen from the NCI database by Hypo01 model screening and molecular docking using the cdocker docking program. Finally, the selected compounds exhibited the identified pharmacophoric features and had a high interaction energy between the ligand and the receptor. Eighty-three potential inhibitors for Chk2 are retrieved for further study.

State-of-the-art and dissemination of computational tools for drug-design purposes: a survey among Italian academics and industrial institutions

During the first edition of the Computationally Driven Drug Discovery meeting, held in November 2011 at Dompé Pharma (L'Aquila, Italy), a questionnaire regarding the diffusion and the use of computational tools for drug-design purposes in both academia and industry was distributed among all participants. This is a follow-up of a previously reported investigation carried out among a few companies in 2007. The new questionnaire implemented five sections dedicated to: research group identification and classification; 18 different computational techniques; software information; hardware data; and economical business considerations. In this article, together with a detailed history of the different computational methods, a statistical analysis of the survey results that enabled the identification of the prevalent computational techniques adopted in drug-design projects is reported and a profile of the computational medicinal chemist currently working in academia and pharmaceutical companies in Italy is highlighted.

Synthesis and biological assessment of novel 2-thiazolylhydrazones and computational analysis of their recognition by monoamine oxidase B

Monoamine oxidase B (MAO-B) is a promising target for the treatment of neurodegenerative disorders. We report the synthesis and the biological evaluation of halogenated derivatives of 1-aryliden-2-(4-phenylthiazol-2-yl)hydrazines. The fluorinated series shows interesting activity and great selectivity toward the human recombinant MAO-B isoform expressed in baculovirus infected BTI insect cells. The multiple crystal structures alignment of the enzyme highlighted pronounced induced fit (IF) adaptations with respect to bound ligands. Therefore, IF docking (IFD) experiments and molecular dynamic (MD) simulations were carried out to reveal the putative binding mode and to explain the experimentally observed differences in the activity of 1-(aryliden-2-(4-(4-chlorophenyl)thiazol-2-yl)hydrazines. The importance of water molecules within the binding site was also investigated. These are known to play an important role in the binding site cavity and to mediate protein-ligand interactions. Detailed analyses of the trajectories provide insights on the chemical features required for the activity of this scaffold. In particular it was highlighted the importance of fluorine atom interacting with the water close to the cofactor and the influence of steric bulkiness of substituents in the arylidene moiety. Free energy perturbation (FEP) analysis confirmed experimental data. The information we deduced will help to develop novel high-affinity MAO-B inhibitors.

Design of novel FLT-3 inhibitors based on dual-layer 3D-QSAR model and fragment-based compounds in silico

FMS-like tyrosine kinase 3 (FLT-3) is strongly correlated with acute myeloid leukemia, but no FLT-3-inhibitor cocomplex structure is available to assist the design of therapeutic inhibitors. Hence, we propose a dual-layer 3D-QSAR model for FLT-3 that integrates the pharmacophore, CoMFA, and CoMSIA. We then coupled the model with the fragment-based design strategy to identify novel FLT-3 inhibitors. In the first layer, the previously established model, Hypo02, was evaluated in terms of its correlation coefficient (r), RMS, cost difference, and configuration cost, with values of 0.930, 1.24, 106.45, and 16.44, respectively. Moreover, Fischer's cross-validation test of data generated by Hypo02 yielded a 98% confidence level, and the validation of the testing set yielded a best r value of 0.87. The features of Hypo02 were separated into two parts and then used to screen the MiniMaybridge fragment compound database. Nine novel FLT-3 inhibitors were generated in this layer. In the second layer, Hypo02 was subjected to an alignment rule to generate CoMFA- and CoMSIA-based models, for which the partial least-squares validation method was utilized. The values of q(2), r(2), and predictive r(2) were 0.58, 0.98, and 0.76, respectively, derived from the CoMFA model with steric and electrostatic fields. The CoMSIA model with five different fields yielded values of 0.54, 0.97, and 0.76 for q(2), r(2), and predictive r(2), respectively. The CoMFA and CoMSIA models were used to constrain 3D structures of the nine novel FLT-3 inhibitors. This dual-layer 3D-QSAR model constitutes a valuable tool to easily and quickly screen and optimize novel potential FLT-3 inhibitors for the treatment of acute myeloid leukemia.

Principles of early drug discovery

Developing a new drug from original idea to the launch of a finished product is a complex process which can take 12-15 years and cost in excess of $1 billion. The idea for a target can come from a variety of sources including academic and clinical research and from the commercial sector. It may take many years to build up a body of supporting evidence before selecting a target for a costly drug discovery programme. Once a target has been chosen, the pharmaceutical industry and more recently some academic centres have streamlined a number of early processes to identify molecules which possess suitable characteristics to make acceptable drugs. This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.