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Neural processes (NPs) are models for meta-learning which output uncertainty estimates. So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. In particular, applications in scientific research may prove especially challenging due to the potential novelty of meta-testing tasks. Molecular property prediction is one such research area that is characterized by sparse datasets of many functions on a shared molecular space. In this paper, we study the application of graph NPs to molecular property prediction with DOCKSTRING, a diverse dataset of docking scores. Graph NPs show competitive performance in few-shot learning tasks relative to supervised learning baselines common in chemoinformatics, as well as alternative techniques for transfer learning and meta-learning. In order to increase meta-generalization to divergent test functions, we propose fine-tuning strategies that adapt the parameters of NPs. We find that adaptation can substantially increase NPs' regression performance while maintaining good calibration of uncertainty estimates. Finally, we present a Bayesian optimization experiment which showcases the potential advantages of NPs over Gaussian processes in iterative screening. Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting. SCIENTIFIC CONTRIBUTION: Neural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. We evaluate their performance on regression and optimization molecular tasks using docking scores, finding them to outperform classical single-task and transfer-learning models. We examine the issue of generalization to divergent test tasks, which is a general concern of meta-learning algorithms in science, and propose strategies to alleviate it., (© 2024. The Author(s).)

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

The phosphatidylinositol 3-kinase Vps34 is part of several protein complexes. The structural organization of heterotetrameric complexes is starting to emerge, but little is known about organization of additional accessory subunits that interact with these assemblies. Combining hydrogen-deuterium exchange mass spectrometry (HDX-MS), X-ray crystallography and electron microscopy (EM), we have characterized Atg38 and its human ortholog NRBF2, accessory components of complex I consisting of Vps15-Vps34-Vps30/Atg6-Atg14 (yeast) and PIK3R4/VPS15-PIK3C3/VPS34-BECN1/Beclin 1-ATG14 (human). HDX-MS shows that Atg38 binds the Vps30-Atg14 subcomplex of complex I, using mainly its N-terminal MIT domain and bridges the coiled-coil I regions of Atg14 and Vps30 in the base of complex I. The Atg38 C-terminal domain is important for localization to the phagophore assembly site (PAS) and homodimerization. Our 2.2 Å resolution crystal structure of the Atg38 C-terminal homodimerization domain shows 2 segments of α-helices assembling into a mushroom-like asymmetric homodimer with a 4-helix cap and a parallel coiled-coil stalk. One Atg38 homodimer engages a single complex I. This is in sharp contrast to human NRBF2, which also forms a homodimer, but this homodimer can bridge 2 complex I assemblies.

Background: Papillary fibroelastoma is a rare benign tumor characterized morphologically since first being described in 1976. Nevertheless, this tumor can be presented with a variety of clinical manifestations, making diagnosis challenging for the physician. There are no gender or age preferences but it is diagnosed by site of presentation along with macro- and microscopic characteristics., Case Report: We report the case of a male patient with a history of type 2 diabetes mellitus and arterial hypertension who was admitted to the hospital with a diagnosis of ischemic heart disease accompanied by sustained ventricular tachycardia. Echocardiogram reported degree I diastolic dysfunction, apical ventricular aneurysm, and unusual apical tumor of the septum and left ventricle., Discussion: Primary heart tumors have an incidence of 0.0017%. The most common symptoms are chest pain, syncope, dyspnea and arrhythmias. Diagnosis is accomplished incidentally by echocardiography, which is usually carried out for other reasons. Surgical procedure of choice is total tumor resection along with valve repair or replacement, if necessary, and in some cases cardiac endothelium resection and repair, with or without pericardium patch replacement., Conclusions: Papillary fibroelastoma is rare but is a recognized cause of embolic phenomena. Rapid identification followed by surgical resection is curative, precise and well tolerated by the patient.