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Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible nature of molecules, which continuously interconvert across conformations via chemical bond rotations and minor vibrational perturbations. To better account for molecular flexibility, some recent works formulate MRL as an ensemble learning problem, focusing on explicitly learning from a set of conformer structures. However, most of these studies have limited datasets, tasks, and models. In this work, we introduce the first MoleculAR Conformer Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of learning on con- former ensembles and suggest promising research directions. MARCEL includes four datasets covering diverse molecule- and reaction-level properties of chemically diverse molecules including organocatalysts and transition-metal catalysts, extending beyond the scope of common GNN benchmarks that are confined to drug-like molecules. In addition, we conduct a comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D MRL models, along with two strategies that explicitly incorporate conformer ensembles into 3D models. Our findings reveal that direct learning from an accessible conformer space can improve performance on a variety of tasks and models. 

Providing drug products for pediatric patients is a challenging problem for the pharmaceutical industry. Children often require flexible low-dose medication with features like taste-masking and ease of swallowing. In recent years, mini-tablets have emerged as an attractive dosing solution that can meet these requirements. They are small form oral dosages around 2-4 mm in diameter that can be dispensed individually or in combination. Conventionally, they are made using methods like direct compression and hot melt extrusion. This study introduces a new technique to make mini-tablets: drop-on-demand 3D printing. Here the active ingredient is suspended in a liquid excipient, the formulation is printed as droplets and each drop is solidified to yield a mini-tablet. An optimal solvent bath that can uniformly capture mini-tablets is designed and dosages of Atorvastatin (active ingredient) are produced as a test case. Quality of these dosages is determined by measuring their content uniformity. 

Neonicotinoids (NNs) are commonly found throughout the environment on surfaces such as seeds, soil, vegetation, and blowing dust particles. However, there is a paucity of data on the kinetics and oxidation products formed on contact with the atmosphere which limits understanding of their potentially far-reaching impacts. In this study, in situ attenuated total reflectance (ATR) FTIR spectroscopy was used to investigate the OH oxidation of thin films of three solid NNs, imidacloprid (IMD), dinotefuran (DNF) and clothianidin (CLD) at 295 ± 3 K. The experimentally measured reaction probabilities based on initial rates of NN loss are (1.6 ± 0.8) × 10 −2 for IMD, (1.5 ± 0.6) × 10 −2 for DNF and (0.9 ± 0.2) × 10 −2 for CLD (±1 σ ), suggesting initial NN lifetimes with respect to OH of 10–17 days. The kinetics were interpreted using a multiphase kinetics model, KM-SUB, which showed that the OH uptake and reaction occurred primarily in the surface layer. Products identified by mass spectrometry included carbonyl-, alcohol- and olefin-containing species formed via hydrogen abstraction from aliphatic C–H groups. Additionally, carbonyl-containing desnitro and urea derivative products were observed from secondary reactions of the initially formed photodegradation products. Reaction with OH will contribute to NN loss both during the day as well as at night when there are non-photolytic sources of this radical. Thus, OH reactions with both the parent neonicotinoid and its photodegradation products should be considered in assessing their environmental impacts.