Finding the most potent compounds using active learning on molecular pairs.

Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and model exploitation might lead to analog identification with limited scaffold diversity. Here, we present ActiveDelta, an adaptive approach that leverages paired molecular representations to predict improvements from the current best training compound to prioritize further data acquisition. We apply the ActiveDelta concept to both graph-based deep (Chemprop) and tree-based (XGBoost) models during exploitative active learning for 99 K benchmarking datasets.

Leveraging bounded datapoints to classify molecular potency improvements.

Molecular machine learning algorithms are becoming increasingly powerful at predicting the potency of potential drug candidates to guide molecular discovery, lead series prioritization, and structural optimization. However, a substantial amount of inhibition data is bounded and inaccessible to traditional regression algorithms. Here, we develop a novel molecular pairing approach to process this data.

The landscape of small-molecule prodrugs.

Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development.

Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.

In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the development of oral drugs with greater bioavailability. Here we show that the interaction profiles between drugs and intestinal drug transporters can be obtained by modulating transporter expression in intact porcine tissue explants via the ultrasound-mediated delivery of small interfering RNAs and that the interaction profiles can be classified via a random forest model trained on the drug-transporter relationships. For 24 drugs with well-characterized drug-transporter interactions, the model achieved 100% concordance.

DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.

Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules, limiting their ability to learn from smaller datasets and to directly compare the anticipated properties of two molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization, especially for tasks with limited available data.

Neuromuscular Development and Disease: Learning From and Models.

The neuromuscular junction (NMJ) is a specialized cholinergic synaptic interface between a motor neuron and a skeletal muscle fiber that translates presynaptic electrical impulses into motor function. NMJ formation and maintenance require tightly regulated signaling and cellular communication among motor neurons, myogenic cells, and Schwann cells. Neuromuscular diseases (NMDs) can result in loss of NMJ function and motor input leading to paralysis or even death.

Using Edpuzzle to improve student experience and performance in the biochemistry laboratory.

Student preparation has been shown to be of appreciable importance to student success in laboratory components of science courses. To promote student engagement prior to each laboratory session, technique and content videos, online assessments, and additional methods aiming to decrease cognitive load have been put into effect. Edpuzzle allows for instructors to effectively combine all of these components on a free, user-friendly, cloud-based platform.