In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism.

Non-Small Cell Lung Cancer (NSCLC) is a prevalent and deadly form of lung cancer worldwide with a low 5-year survival rate. Current treatments have limitations, particularly for advanced-stage patients. P21, a protein that inhibits the CCND1-CDK4 complex, plays a crucial role in cell proliferation. Computer-Aided Drug Design (CADD) based on pharmacophores can screen and design PPI inhibitors targeting the CCND1-CDK4 complex. By analyzing known inhibitors, key pharmacophores are identified, and computational methods are used to screen potential PPI inhibitors. Molecular docking, pharmacophore matching, and structure-activity relationship studies optimize the inhibitors. This approach accelerates the discovery of CCND1-CDK4 PPI inhibitors for NSCLC treatment. Molecular dynamics simulations of CCND1-CDK4-P21 and CCND1-CDK4 complexes showed stable behavior, comprehensive sampling, and P21's impact on complex stability and hydrogen bond formation. A pharmacophore model facilitated virtual screening, identifying compounds with favorable binding affinities. Further simulations confirmed the stability and interactions of selected compounds, including 513457. This study demonstrates the potential of CADD in optimizing PPI inhibitors targeting the CCND1-CDK4 complex for NSCLC treatment. Extended simulations and experimental validations are necessary to assess their efficacy and safety.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores.

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening.

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds' databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

Network pharmacology-based prediction of the active ingredients, potential targets, and signaling pathways in compound Lian-Ge granules for treatment of diabetes.

AIMS: Compound Lian-Ge granules (CLGGs) is a traditional Chinese medicine preparation with good hypoglycemic effect and health function. This study was to predict its active ingredients, potential targets, signaling pathways, and investigate its mechanism of "ingredient-targets-pathways." METHODS: Pharmacodynamics studies on diabetic rats showed that CLGGs had an obvious hypoglycemic effect. On this basis, 27 hypoglycemic active ingredients were screened out. Their targets were confirmed by comparing with these hypoglycemic targets in PharmMapper and DrugBank databases via reversed pharmacophore matching approach. The relationships between ingredients and targets were revealed by comparing data in the String database. A network of "ingredient-target-passageway" was constructed. RESULTS: Studies showed that CLGGs had 24 active ingredients, ie, berberine, puerarin, danshinolic acid A, and sinigrin, etc. These ingredients involved nine targets, ie, insulin-like growth factor 1 receptor, insulin-degrading enzyme, ɑ-amylase, and so on, and 111 metabolic pathways, eg, hypoxia-inducible factor 1 signaling pathway, PI3K-Akt signaling pathway, mammalian target of rapamycin signaling pathway, and FoxO signaling pathway. CONCLUSION: Using network pharmacology methods, this study predicted the hypoglycemic active ingredients in CLGGs and revealed their targets, and provided a clue for further exploration of the hypoglycemic mechanism of CLGGs.

Consensus Computational Ligand-Based Design for the Identification of Novel Modulators of Human Estrogen Receptor Alpha.

We describe the first targeted validation of fFLASH, a molecular similarity program from IBM that has been previously proposed as suitable for the virtual screening (VS) of compound libraries based on explicit 3D flexible superimpositions, as part of its deployment within a novel consensus ligand-based virtual screening cascade. A virtual screening protocol using fFLASH for the human estrogen receptor alpha (ERα) was advanced and benchmarked against screens completed using established commercial screening softwares - Catalyst and ROCS. The optimised protocol was applied to a ∼6000 member physical screening collection and virtual 'hits' sourced and biologically assayed. The approach identified a novel, potent and highly selective partial antagonist of the ERα. This study firstly validates the clique detection algorithm utilised by fFLASH and secondly, emphasises the benefits of the consensus approach of employing more than one program in a VS protocol.