Alex T. Müller

Generative Recurrent Networks for De Novo Drug Design

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short‐term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine‐tuned the RNN′s predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN‐LSTM system for high‐impact use cases, such as low‐data drug discovery, fragment based molecular design, and hit‐to‐lead optimization for diverse drug targets.

Membranolytic anticancer peptides

Membranolytic anticancer peptides (ACPs) potentially offer new perspectives for the development of anticancer drugs. Their receptor-independent mechanisms of action hold the promise to hinder the development of resistance, which is a hurdle of many present-day chemotherapeutics. Peptide selectivity for cancer cells is believed to be primarily due to a net charge difference between neoplastic and non-neoplastic cells at the membrane surface. However, their exact molecular mechanisms are not yet fully understood. In this review, we summarise the state of the art of membranolytic ACP research and discuss the molecular features that have been related to the structure–activity relationships of alpha-helical ACPs.

Recurrent Neural Network Model for Constructive Peptide Design

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.

Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

We present a “deep” network architecture for chemical data analysis and classification together with a prospective proof‐of‐concept application. The model features a self‐organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two‐dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence‐length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Sparse Neural Network Models of Antimicrobial Peptide-Activity Relationships.

We present an adaptive neural network model for chemical data classification. The method uses an evolutionary algorithm for optimizing the network structure by seeking sparsely connected architectures. The number of hidden layers, the number of neurons in each layer and their connectivity are free variables of the system. We used the method for predicting antimicrobial peptide activity from the amino acid sequence. Visualization of the evolved sparse network structures suggested a high charge density and a low aggregation potential in solution as beneficial for antimicrobial activity. However, different training data sets and peptide representations resulted in greatly varying network structures. Overall, the sparse network models turned out to be less accurate than fully‐connected networks. In a prospective application, we synthesized and tested 10 de novo generated peptides that were predicted to either possess antimicrobial activity, or to be inactive. Two of the predicted antibacterial peptides showed cosiderable bacteriostatic effects against both Staphylococcus aureus and Escherichia coli. None of the predicted inactive peptides possessed antibacterial properties. Molecular dynamics simulations of selected peptide structures in water and TFE suggest a pronounced peptide helicity in a hydrophobic environment. The results of this study underscore the applicability of neural networks for guiding the computer‐assisted design of new peptides with desired properties.

Rational Design of Membrane-Pore-Forming Peptides

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

modlAMP: Python for antimicrobial peptides

Summary: We have implemented the molecular design laboratorys antimicrobial peptides package (modlAMP), a Python‐based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled datasets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra. Availability and Implementation: The modlAMP Python package is available under the BSD license from URL http://doi.org/10.5905/ethz‐1007‐72 or via pip from the Python Package Index (PyPI). Contact: gisbert.schneider@pharma.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Designing Anticancer Peptides by Constructive Machine Learning

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short‐term memory cells was trained on α‐helical cationic amphipathic peptide sequences and then fine‐tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities.