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Metal Chemistry of Alzheimer’s Disease The metal ion hypothesis proposed that AD was associated with dyshomeostasis of metal ions, most particularly Fe, and later Zn and Cu.  Significant advancements at the interface of bioinorganic chemistry and neurology have paved the path for intriguing aims related to zinc homeostasis.  While the concentration of Zn2+ in aggregates is a key factor in Alzheimer\’s disease, the mechanism by which Zn2+/Aβ enhances aggregation is still unknown.  By using MD and REMD simulations, Zinc coordination in A oligomers was discovered to reduce oligomer solvation and so promote Zn2+/ Aβ aggregation, resulting in less uniform structures. simulations revealed Zn2+ ions can bind at various sites in the N-terminal domain, resulting in structural variance with a number of populations. https://lnkd.in/gPm93kBn

Alzheimers…! Was a problem, is a problem, …, would not be a PROBLEM. Another antibody lecanemab-irmb (by Eisai and Biogen), following donanemab (by Eli Lilly), and gantenerumab (by Roche). These antibodies have potential to clear amyloid beta (Aβ) plaque. Image from: https://lnkd.in/d6V5FY2j P.S. This being said, the Official Position of Binary Star is that Alzheimer\’s is not a disease driven by amyloid, but rather amyloid build-up and aggregation is a symptom of a deeper underlying cause. Looking for partners to work with us on early stage mechanism validation. Hundreds of billions have been spent on the Amyloid Hypothesis, it\’s time to spend a bit on something else

Machine learning and AI based methods are rapidly shaping the future technologies. Computer aided drug design is no exception. This review articles summarizes the applications of various machine learning methods in predicting ligand:protein interactions. We, at Binary Star Research Services, are involved in developing and testing machine learning based methods for predicting the activating ligands for specific GPCRs. We welcome any collaborations from Pharma industry for harnessing these rapidly evolving technologies in context of drug design. https://lnkd.in/gh8YdHQV

Rigorous absolute binding free energy (ABFE) calculations are increasingly gaining attention as an important component of modern virtual screening (VS) efforts. This article from Prof. Gilson\’s lab nicely illustrates how enrichment of actives can be improved using ABFE methods. We at Binary Star Research Services routinely employ ABFE methods in VS projects. We always welcome pharmaceutical companies for discussions on their computational chemistry requirements. We are always looking for establishing new industry collaborations. https://lnkd.in/gWcWE_MF

Pan-assay interference compounds, or PAINS, are a specific group of compounds with defined structures that fall into several classes. However, these compounds are often not recognized by biologists and less experienced chemists. They are often reported as having potent activity against a wide range of proteins, leading to wasted time and resources in trying to improve their activity. This results in chemists creating multiple variations of these compounds in hopes of finding a better fit with proteins while overlooking other compounds with real potential. The binary star database is a carefully curated library of millions of compounds that have good ADME properties, free of Pan-Assay Interference compounds (PAINS), and free of unwanted functionalities. Credit: Illustration by Roz Chast

The FDA approved the cMet/ALK dual inhibitor Crizotinib in 2011. In human cancers, the overexpression of the c-Met protein has frequently been found. Therefore, c-MET is a desirable and promising anticancer target. The initial step of CADD was to explore kinase inhibitors, specifically indolin-2-one derivatives, for c-MET inhibition. The intense anti-c-MET activity was demonstrated by PHA-665752. But its negative drug-like aspects, unfortunately, prevented further research on the compound. Its co-crystal with c-MET revealed the critical inhibitor binding site. One of the recently created compounds showed remarkable c-MET inhibition. improving the binding effectiveness by lipophilic efficiency and employing structure-based design techniques upon docking the structure lead to benzyloxy and aminopyridine group optimization and chiral centre evaluation. Finally, Crizotinib, an effective tumour growth inhibitor has been achieved. Overall, the use of ML in drug discovery is still a relatively new field, and there is a lot of ongoing research to explore its full potential. Photo and Source: https://lnkd.in/gEUe2MKN

In the field of drug discovery, ML can be used to analyze large amounts of data from various sources, including genomic data, chemical data, and data from previous clinical trials. This can help identify new targets for drugs and predict the potential efficacy of drug candidates. Overall, the use of ML in drug discovery is still a relatively new field, and there is a lot of ongoing research to explore its full potential. Photo and Source: Machine Learning in Drug Discovery: A Review – Artificial Intelligence Review link.springer.com

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