Taking a Respite from Representation Learning for Molecular Property Prediction

Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite the boom of AI techniques in molecular representation learning, some key aspects underlying molecular property prediction haven't been carefully examined yet. In this study, we conducted a systematic comparison on three representative models, random forest, MolBERT and GROVER, which utilize three major molecular representations, extended-connectivity fingerprints, SMILES strings and molecular graphs, respectively. Notably, MolBERT and GROVER, are pretrained on large-scale unlabelled molecule corpuses in a self-supervised manner. In addition to the commonly used MoleculeNet benchmark datasets, we also assembled a suite of opioids-related datasets for downstream prediction evaluation. We first conducted dataset profiling on label distribution and structural analyses; we also examined the activity cliffs issue in the opioids-related datasets. Then, we trained 4,320 predictive models and evaluated the usefulness of the learned representations. Furthermore, we explored into the model evaluation by studying the effect of statistical tests, evaluation metrics and task settings. Finally, we dissected the chemical space generalization into inter-scaffold and intra-scaffold generalization and measured prediction performance to evaluate model generalizbility under both settings. By taking this respite, we reflected on the key aspects underlying molecular property prediction, the awareness of which can, hopefully, bring better AI techniques in this field

Synthesis and characterization of biodegradable poly(ester anhydride) based on -caprolactone and adipic anhydride initiated by potassium poly(ethylene glycol)ate

Novel biodegradable poly(ester anhydride) block copolymers based on -caprolactone (-CL) and adipic anhydride (AA) were prepared by sequential polymerization. -CL was first initiated by potassium poly(ethylene glycol)ate and polymerized into active chains (PCL-O-K+), which were then used to initiate the ring-opening polymerization of AA. The effects of the AA feed ratio, solvent polarity, monomer concentration, and temperature on sequential polymerization were investigated. The copolymers, obtained under different conditions, were characterized by Fourier transform infrared, 1H NMR, gel permeation chromatography (GPC), and differential scanning calorimetry (DSC). The GPC results showed that the weight-average molecular weights of the block copolymers were approximately 6.0 × 104. The DSC results indicated the immiscibility of the two component

An Extremely Simple and Effective Strategy to Tailor the Surface Performance of Inorganic Substrates by Two New Photochemical Reactions

This article reports on a new sequential strategy to fabricate monolayer functional organosilane films on inorganic substrate surfaces, and subsequently, to pattern them by two new photochemical reactions. (1) By using UV light (254 nm) plus dimethylformamide (DMF), a functional silane monolayer film could be fabricated quickly (within minutes) under ambient temperature. (2) The organic groups of the formed films became decomposed in a few minutes with UV irradiation coupled with a water solution of ammonium persulfate (APS). (3) When two photochemical reactions were sequentially combined, a high-quality patterned functional surface could be obtained thanks to the photomask

Self-Stabilized Precipitation Polymerization and Its Application

An effective, value-added use of the large amounts of olefinic compounds produced in the processing of petroleum, aside from ethylene and propylene, has been a long outstanding challenge. Here, we developed a novel heterogeneous polymerization method, beyond emulsion/dispersion/suspension, termed self-stabilized precipitation (2SP) polymerization, which involves the nucleation and growth of nanoparticles (NPs) of a well-defined size without the use of any stabilizers and multifunctional monomers (crosslinker). This technique leads to two revolutionary advances: (1) the generation of functional copolymer particles from single olefinic monomer or complex olefinic mixtures (including C4/C5/C9 fractions) in large quantities, which open a new way to transform huge amount of unused olefinic compounds in C4/C5/C9 fractions into valuable copolymers, and (2) the resultant polymeric NPs possess a self-limiting size and narrow size distribution, therefore being one of the most simple, efficient, and green strategies to produce uniform, size-tunable, and functional polymeric nanoparticles. More importantly, the separation of the NPs from the reaction medium is simple and the supernatant liquid can be reused; hence this new synthetic strategy has great potential for industrial production