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A robust DNA-compatible Wittig reaction mediated by PPh2CH3 has been validated for DNA-conjugated α-chloroacetamides with aldehydes and, alternatively, DNA-conjugated aldehydes with α-halo acetamides or ketones. Further, 2-aminopyridines were acylated with α-chloroacetyl chloride and then reacted with DNA-conjugated aldehydes. Lastly, a pilot library employing our optimized Wittig reaction protocol was synthesized. The ability to generate α,ß-unsaturated carbonyl compounds may be particularly useful for the design of DNA-encoded libraries capable of covalently interacting with protein targets.

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Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

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Water molecules play an important role in modeling protein-ligand interactions. However, traditional molecular docking methods often ignore the impact of the water molecules by removing them without any analysis or keeping them as a static part of the proteins or the ligands. Hence, the accuracy of the docking simulations will inevitably be damaged. Here, we introduce a multi-body docking program which incorporates the fixed or the variable number of the key water molecules in protein-ligand docking simulations. The program employed NSGA II, a multi-objective optimization algorithm, to identify the binding poses of the ligand and the key water molecules for a protein. To this end, a force-field-based hydration-specific scoring function was designed to favor estimate the binding affinity considering the key water molecules. The program was evaluated in aspects of the docking accuracy, cross-docking accuracy, and screening efficiency. When the numbers of the key water molecules were treated as fixed-length optimization variables, the docking accuracy of the multi-body docking program achieved a success rate of 80.58% for the best RMSD values for the recruit of the ligands smaller than 2.0 Å. The cross-docking accuracy was investigated on the presence and absence of the key water molecules by four protein targets. The screening efficiency was assessed against those protein targets. Results indicated that the proposed multi-body docking program was with good performance compared with the other programs. On the other side, when the numbers of the key water molecules were treated as variable-length optimization variables, the program obtained comparative performance under the same three evaluation criterions. These results indicated that the multi-body docking with the variable numbers of the water molecules was also efficient. Above all, the multi-body docking program developed in this study was capable of dealing with the problem of the water molecules that explicitly participating in protein-ligand binding.

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The synthesis of a series of ribose-modified anilinopyrimidine derivatives was efficiently achieved by utilizing DBU or tBuOLi-promoted coupling of ribosyl alcohols with 2,4,5-trichloropyrimidine as key step. Preliminary biological evaluation of this type of compounds as new EGFR tyrosine kinase inhibitors for combating EGFR L858R/T790M mutant associated with drug resistance in the treatment of non-small cell lung cancer revealed that 3-N-acryloyl-5-O-anilinopyrimidine ribose derivative 1a possessed potent and specific inhibitory activity against EGFR L858R/T790M over WT EGFR. Based upon molecular docking studies of the binding mode between compound 1a and EGFR, the distance between the Michael receptor and the pyrimidine scaffold is considered as an important factor for the inhibitory potency and future design of selective EGFR tyrosine kinase inhibitors against EGFR L858R/T790M mutants.