Ahneman et al. (Reports, 13 April 2018) applied machine learning models to predict C–N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. However, the experimental design is insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.
The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish quantitative structure−activity and structure−property relationships for drug discovery. Now, advances in deep learning make it possible to efficiently and compactly learn molecular representations directly from data. In this review, we discuss how active research in molecular deep learning can address limitations of current descriptors and fingerprints while creating new opportunities in cheminformatics and virtual screening. We provide a concise overview of the role of representations in cheminformatics, key concepts in deep learning, and argue that learning representations provides a way forward to improve the predictive modeling of small molecule bioactivities and properties.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies.Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization.We report a proof-of-concept deep learning pipeline identifying specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotated >70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieved strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualized morphology distributions for WSIs at high resolution. Resulting plaque-burden scores correlated well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrated that networks learned patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability may suggest a route to neuropathologic deep phenotyping. KeywordsMachine learning, Alzheimer's disease, amyloid, deep learning, deep phenotyping, interpretability, explainable AI, convolutional neural networks, dementia, immunohistochemistry, plaques, pathology.Extracellular deposition of amyloid beta (Aβ) plaques is a pathological hallmark of Alzheimer's disease (AD) 1,2 , a common neurodegenerative disease. Amyloid plaques have a diverse range of morphologies and neuroanatomic distributions 1 . The current consensus criteria for a neuropathological diagnosis of AD [3][4][5] incorporate protocols assessing plaque density and distribution; some researchers have hypothesized that plaques may be an initiating event in AD 5,6 . More precise measures of plaque morphologies (such as cored, neuritic, and diffuse) can serve as a basis for understanding disease progression and pathophysiology, providing guidance and insight into disease mechanisms 2,7-10 .For neuropathologic diagnosis, established semi-quantitative scales are used to assess plaque burden ( Fig. 1a ) 4,8,11,12 . The standard semi-quantitative criteria put forth by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) based on the manual assessment of the highest density of neocortical neuritic plaques 4,13 . Diffuse plaques, which may be the initial morphological type of Aβ 14,15 , can account for over 50% of plaque burden in preclinical cases but are not included in CERAD 16 . Furthermore, data on anatomical location ( i.e. Thal amyloid phase) are based on the presence of plaques regardless of type or density 5 . The potential for neuropathologic deep phenotyping efforts that account for anatomic location, diverse sources of proteinopathy, and quantitative pathology densities motivates the development of effective and scalable quantitative methods to differentiate pathological subty...
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