Bio-inspired Chemical Space Exploration of Terpenoids

Many computational methods are used to expand the open-ended border of chemical spaces. Natural products and their derivatives are an important source for drug discovery, and some algorithms are devoted to rapidly generating pseudo-natural products, while their accessibility and chemical interpretation were often ignored or underestimated, thus hampering experimental synthesis in practice. Herein, a bio-inspired strategy (named TeroGen) is proposed, in which the cyclization and decoration stage of terpenoid biosynthesis were mimicked by meta-dynamics simulations and deep learning models respectively, to explore their chemical space. In the protocol of TeroGen, the synthetic accessibility is validated by reaction energetics (reaction barrier and reaction heat) based on the GFN2-xTB methods. Chemical interpretation is an intrinsic feature as the reaction pathway is bioinspired and triggered by the RMSD-PP method in conjunction with an encoder-decoder architecture. This is quite distinct from conventional library/fragment-based or rule-based strategies, by using TeroGen, new reaction routes are feasibly explored to increase the structural diversity. For example, only a rather limited number of sesterterpenoids in our training set is included in this work, but our TeroGen would predict more than 30000 sesterterpenoids and map out the reaction network with super efficiency, ten times as many as the known sesterterpenoids (less than 2500). In sum, TeroGen not only greatly expands the chemical space of terpenoids but also provides various plausible biosynthetic pathways, which are crucial clues for heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids.