Introduction to artificial intelligence and deep learning using interactive electronic programming notebooks.

Artificial intelligence (AI), or deep learning (DL), approaches have already found their way into our everyday lives. Furthermore, these methods are a central part of research in the life and natural sciences and have been applied in the form of machine learning for decades. In pharmaceutical and medicinal chemistry, and in computer-aided drug discovery, current developments are also changing the way drugs are developed. It is essential to familiarize students with AI methods already during their studies and prepare them for future tasks and challenges. We developed a set of interactive learning materials based on cheminformatics examples that can be used to establish such introductory AI courses in the life and natural sciences. These interactive notebooks are easily accessible without the need for installation, and no prior programming knowledge is required. Through these notebooks, students can easily study how AI/DL works and how these methods can be applied. This knowledge will foster a general competence when interacting with and evaluating future DL applications later in their career. The materials are freely available and publicly accessible through a GitHub repository in German and English.

Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods have evolved into important and invaluable approaches for studying ion channels and their functions. This is mainly due to their demanding mechanism of action where a static picture of an ion channel structure is often insufficient to fully understand the underlying mechanism. Therefore, the use of computational methods is as important as chemical-biological based experimental methods for a better understanding of ion channels. This review provides an overview on a variety of computational methods and software specific to the field of ion-channels. Artificial intelligence (or more precisely machine learning) approaches are applied for the sequence-based prediction of ion channel family, or topology of the transmembrane region. In case sufficient data on ion channel modulators is available, these methods can also be applied for quantitative structureactivity relationship (QSAR) analysis. Molecular dynamics (MD) simulations combined with computational molecular design methods such as docking can be used for analysing the function of ion channels including ion conductance, different conformational states, binding sites and ligand interactions, and the influence of mutations on their function. In the absence of a three-dimensional protein structure, homology modelling can be applied to create a model of your ion channel structure of interest. Besides highlighting a wide range of successful applications, we will also provide a basic introduction to the most important computational methods and discuss best practices to get a rough idea of possible applications and risks.

Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-Based Virtual Screening.

Similarity-based virtual screening is a fundamental tool in the early drug discovery process and relies heavily on molecular fingerprints. We propose a novel strategy of generating domain-specific fingerprints by training neural networks on target-specific bioactivity datasets and using the activation as a new molecular representation. The neural network is expected to combine information of already known bioactive compounds with unique information of the molecular structure and by doing so enrich the fingerprint. We evaluate this strategy on a large kinase-specific bioactivity dataset. A comparison of five neural network architectures and their fingerprints to the well-established extended-connectivity fingerprint (ECFP) and an autoencoder shows that our neural fingerprint produces better results in the similarity search. Most importantly, the neural fingerprint performs well even when specific targets are not included during training. Surprisingly, while Graph Neural Networks (GNNs) are thought to offer an advantageous alternative, the best performing neural fingerprints were based on traditional fully connected layers using the ECFP4 as the input. The neural fingerprint is freely available at: https://github.com/kochgroup/kinase_nnfp.

Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification.

Siamese networks, representing a novel class of neural networks, consist of two identical subnetworks sharing weights but receiving different inputs. Here we present a similarity-based pairing method for generating compound pairs to train Siamese neural networks for regression tasks. In comparison with the conventional exhaustive pairing, it reduces the algorithm complexity from O(n2) to O(n). It also results in a better prediction performance consistently on the three physicochemical datasets, using a multilayer perceptron with the circular fingerprint as a proof of concept. We further include into a Siamese neural network the transformer-based Chemformer, which extracts task-specific features from the simplified molecular-input line-entry system representation of compounds. Additionally, we propose a means to measure the prediction uncertainty by utilizing the variance in predictions from a set of reference compounds. Our results demonstrate that the high prediction accuracy correlates with the high confidence. Finally, we investigate implications of the similarity property principle in machine learning.

Natural product scores and fingerprints extracted from artificial neural networks.

Due to their desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. In-depth analysis showed that the extracted natural product-specific neural fingerprint outperforms traditional as well as natural product-specific fingerprints on three datasets. Further, we explored how the activations from the output layer of a network can work as a novel natural product likeness score. Overall, two natural product-specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.