Machine Learning Methods for Small Data Challenges in Molecular Science

Abstract

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Next Generation of Mass Spectrometry Imaging: from Micrometer to Subcellular Resolution

“…The applications in image analysis, biological information clustering and extraction, drug discovery, and disease prediction provide great inspiration for the processing of MSI data. In recent years, the vigorous development of proteomics, lipomics, and metabolomics is also attributed to the continuous advancement of machine learning algorithms. − Nevertheless, the research mainly focuses on tissue sections or single cells detached from the tissue environment due to the spatial resolution of micrometers. For instance, Caroline R. Bartman et al measured the TCA flux and ATP production in solid tumors and they discovered the obvious difference between primary and metastatic solid tumors .…”

Integrated Molecular Modeling and Machine Learning for Drug Design

“…As computational tools at all stages of drug discovery and development have been extensively reviewed recently, , in this perspective, we focus on the recent progress related to our group. Specifically, we first summarize protein pocket identification and analysis methods based on fragment-centric topographical mapping.…”

“…49−60 The accurate prediction of molecular properties requires models to learn more robust molecular representations for 1D SMILES, 61 2D graph, 62 and 3D geometry. 63 As computational tools at all stages of drug discovery and development have been extensively reviewed recently, 64,65 in this perspective, we focus on the recent progress related to our group. Specifically, we first summarize protein pocket identification and analysis methods based on fragment-centric topographical mapping.…”

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

“…It is then not surprising that the recent explosive development of machine learning (ML) techniques, including deep neural networks (DNNs), − is already making a noticeable impact on this field, − including works aimed directly at improving the accuracy of the description of complex solvation effects. − It should be noted that the majority of these recent works combine quantum mechanics (QM)-based methodology with ML, while our interest here is purely classical approaches. Among recent purely ML-based approaches, a featurization algorithm, based on functional class fingerprints and implemented within the DeepChem ML framework, was used in ref to predict the hydration-free energies (HFEs) of a diverse set of 642 neutral small molecules available in FreeSolvarguably the largest public database of experimentally measured HFEs.…”