AI News, Neural Network Learns to Select Potential Anticancer Drugs

Neural Network Learns to Select Potential Anticancer Drugs

Scientists from Mail.Ru Group, Insilico Medicine and MIPT have for the first time applied a generative neural network to create new pharmaceutical medicines with the desired characteristics.

By using Generative Adversarial Networks (GANs) developed and trained to 'invent' new molecular structures, there may soon be a dramatic reduction in the time and cost of searching for substances with potential medicinal properties.

For example, pharmacologists might continue to research aspirin that has already been in use for many years, perhaps adding something into the compound to reduce side effects or increase efficiency, yet the substance still remains the same.

But now they have focused on a much more challenging question: Is there a chance to create conceptually new molecules with medicinal properties using the novel flavor of deep neural networks trained on millions of molecular structures?

The encoder worked with the decoder to compress and then restore information on the parent compound, while the discriminator helped make the compressed presentation more suitable for subsequent recovery.

Once the network learned a wide swath of known molecules, the encoder and discriminator 'switched off', and the network generated descriptions of the molecules on its own using the decoder.

Developing Generative Adversarial Networks that produce high-quality images based on textual inputs requires substantial expertise and lengthy training time on high-performance computing equipment.

As a result, a 'black box' capable of producing a specified output for the specified input was created, after which the developers removed the first layers, and the network generated the fingerprints by itself when the information was run through again.

According to one of the authors of the research, Alex Zhavoronkov, the founder of Insilico Medicine and international adjunct professor at MIPT, 'Unlike the many other popular methods in deep learning, Generative Adversarial Networks (GANs) were proposed only recently, in 2014, by Ian Goodfellow and Yoshua Bengio's group and scientists are still exploring its power in generating meaningful images, videos, works of art and even music.

Molecular Fingerprint-based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions

They are regarded as essential tools in pharmaceutical industries to identify and generate high quality leads in the early stages of drug discovery.1–3 Several QSAR methodologies have been developed since the concept was first introduced by Free, Wilson, Hansch and Fujita.4, 5 Traditional 2D-QSAR methods such as Free-Wilson and Hansch-Fujita models use the presence and absence of molecular fragments or ligands' physicochemical properties to perform quantitative predictions.

2D and 3D molecular descriptors of molecular physical properties were used as neural network inputs to predict molecular properties or biological endpoints in several case studies such as anti-diabetes, anti-cancer and anti-HIV research.24–26 However, to the best of our knowledge, there are no studies which use molecular fingerprints as descriptors in developing ANN-QSAR models to predict biological activities (such as pIC50 or pKi) of chemical ligands although there are a few studies reported to predict ligand classes.27, 28 In this work, we used three types of molecular fingerprints to train ANN-QSAR models, namely fingerprint-based ANN-QSAR (FANN-QSAR), and the results were compared to known 2D and 3D QSAR methods using five data sets.

In fact, cannabinoid drug research is experiencing a great challenge as the first CB1 antagonist drug, Rimonabant, launched in 2006 as an anorectic/anti-obesity drug, was recently withdrawn from the European market due to the complications of suicide and depression side effects.29 As we know, structure-based design of novel CB2 ligands that do not confer psychotropic side effects is hindered because of a lack of information about experimental 3D receptors structures, which is true, in general, for all drug discovery research involving G-protein coupled receptors (GPCRs).

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