Abstract
Macrolactones, macrocyclic lactones with at least twelve atoms within the core ring, include diverse natural products such as macrolides with potent bioactivities (e.g. antibiotics) and useful drug-like characteristics. We have developed MacrolactoneDB, which integrates nearly 14,000 existing macrolactones and their bioactivity information from different public databases, and new molecular descriptors to better characterize macrolide structures. The chemical distribution of MacrolactoneDB was analyzed in terms of important molecular properties and we have utilized three targets of interest (Plasmodium falciparum, Hepatitis C virus and T-cells) to demonstrate the value of compiling this data. Regression machine learning models were generated to predict biological endpoints using seven molecular descriptor sets and eight machine learning algorithms. Our results show that merging descriptors yields the best predictive power with Random Forest models, often boosted by consensus or hybrid modeling approaches. Our study provides cheminformatics insights into this privileged, underexplored structural class of compounds with high therapeutic potential.
Highlights
Macrocycles are at least 12-membered ring structures[1,2]
To assess the chemical diversity of molecular properties displayed by macrolactones, we analyzed MacrolactoneDB by studying the distribution of important molecular properties (MW, SlogP, TPSA, hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), NRB, ring size (RS))
Compounds from MacrolactoneDB can be used as scaffolds that can be manipulated and modified to design novel macrolides using biosynthetic engineering methods, semi-synthesis or traditional organic chemistry
Summary
Macrocycles are at least 12-membered ring structures[1,2]. Of particular interest are macrolides and macrolactones, a privileged structural class commonly found in bioactive natural products[3,4] and widely researched in pharmaceutical drug discovery[1,2,3,5,6,7,8,9] (Fig. 1). Macrocyclic structures are interesting because of their ability to bind to difficult, undruggable protein targets, and display unusual physicochemical properties[5]. Studying this structural class could yield important findings to help identify essential characteristics for novel macrolactone drug design. Distinguishing aspects of cyclic drugs are their rigidity which reduces undesirable side effects, the associated entropic costs to increase binding affinity, stability to proteolytic degradation, ability to bind to difficult targets with large binding pockets[5,6,7,12] and ‘chameleonic’ ability to flip conformations. Traditional organic synthetic approaches towards macrocyclic compounds have proven extremely challenging, usually involving numerous steps Chemical databases such as ChEMBL13, PubChem[14], ZINC1515 are indispensable to computer-aided drug discovery (CADD). To accommodate research groups focusing on different areas of macrolactones, we constructed a web application with multiple filters on chemical properties such as ring size, number of sugars, molecular weight, etc. to allow users to extract a highly specific subset of interest
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