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

In silico drug absorption tract: An agent-based biomimetic model for human oral drug absorption.

BACKGROUND:An agent-based modeling approach has been suggested as an alternative to traditional, equation-based modeling methods for describing oral drug absorption. It enables researchers to gain a better understanding of the pharmacokinetic (PK) mechanisms of a drug. This project demonstrates that a biomimetic agent-based model can adequately describe the absorption and disposition kinetics both of midazolam and clonazepam. METHODS:An agent-based biomimetic model, in silico drug absorption tract (ISDAT), was built to mimic oral drug absorption in humans. The model consisted of distinct spaces, membranes, and metabolic enzymes, and it was altogether representative of human physiology relating to oral drug absorption. Simulated experiments were run with the model, and the results were compared to the referent data from clinical equivalence trials. Acceptable similarity was verified by pre-specified criteria, which included 1) qualitative visual matching between the clinical and simulated concentration-time profiles, 2) quantitative similarity indices, namely, weighted root mean squared error (RMSE), and weighted mean absolute percentage error (MAPE) and 3) descriptive similarity which requires less than 25% difference between key PK parameters calculated by the clinical and the simulated concentration-time profiles. The model and its parameters were iteratively refined until all similarity criteria were met. Furthermore, simulated PK experiments were conducted to predict bioavailability (F). For better visualization, a graphical user interface for the model was developed and a video is available in Supporting Information. RESULTS:Simulation results satisfied all three levels of similarity criteria for both drugs. The weighted RMSE was 0.51 and 0.92, and the weighted MAPE was 5.99% and 8.43% for midazolam and clonazepam, respectively. Calculated PK parameter values, including area under the curve (AUC), peak plasma drug concentration (Cmax), time to reach Cmax (Tmax), terminal elimination rate constant (Kel), terminal elimination half life (T1/2), apparent oral clearance (CL/F), and apparent volume of distribution (V/F), were reasonable compared to the referent values. The predicted absolute oral bioavailability (F) was 44% for midazolam (literature reported value, 31-72%) and 93% (literature reported value, ≥ 90%) for clonazepam. CONCLUSION:The ISDAT met all the pre-specified similarity criteria for both midazolam and clonazepam, and demonstrated its ability to describe absorption kinetics of both drugs. Therefore, the validated ISDAT can be a promising platform for further research into the use of similar in silico models for drug absorption kinetics