51 research outputs found
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel
molecules with good affinity to the desired biological target. In this work, we
show that recurrent neural networks can be trained as generative models for
molecular structures, similar to statistical language models in natural
language processing. We demonstrate that the properties of the generated
molecules correlate very well with the properties of the molecules used to
train the model. In order to enrich libraries with molecules active towards a
given biological target, we propose to fine-tune the model with small sets of
molecules, which are known to be active against that target.
Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test
molecules that medicinal chemists designed, whereas against Plasmodium
falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled
with a scoring function, our model can perform the complete de novo drug design
cycle to generate large sets of novel molecules for drug discovery.Comment: 17 pages, 17 figure
Utilizing Reinforcement Learning for de novo Drug Design
Deep learning-based approaches for generating novel drug molecules with
specific properties have gained a lot of interest in the last few years. Recent
studies have demonstrated promising performance for string-based generation of
novel molecules utilizing reinforcement learning. In this paper, we develop a
unified framework for using reinforcement learning for de novo drug design,
wherein we systematically study various on- and off-policy reinforcement
learning algorithms and replay buffers to learn an RNN-based policy to generate
novel molecules predicted to be active against the dopamine receptor DRD2. Our
findings suggest that it is advantageous to use at least both top-scoring and
low-scoring molecules for updating the policy when structural diversity is
essential. Using all generated molecules at an iteration seems to enhance
performance stability for on-policy algorithms. In addition, when replaying
high, intermediate, and low-scoring molecules, off-policy algorithms display
the potential of improving the structural diversity and number of active
molecules generated, but possibly at the cost of a longer exploration phase.
Our work provides an open-source framework enabling researchers to investigate
various reinforcement learning methods for de novo drug design
Transformer-based molecular optimization beyond matched molecular pairs
Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist\u27s intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule
Autonomous Drug Design with Multi-Armed Bandits
Recent developments in artificial intelligence and automation support a new
drug design paradigm: autonomous drug design. Under this paradigm, generative
models can provide suggestions on thousands of molecules with specific
properties, and automated laboratories can potentially make, test and analyze
molecules with minimal human supervision. However, since still only a limited
number of molecules can be synthesized and tested, an obvious challenge is how
to efficiently select among provided suggestions in a closed-loop system. We
formulate this task as a stochastic multi-armed bandit problem with multiple
plays, volatile arms and similarity information. To solve this task, we adapt
previous work on multi-armed bandits to this setting, and compare our solution
with random sampling, greedy selection and decaying-epsilon-greedy selection
strategies. According to our simulation results, our approach has the potential
to perform better exploration and exploitation of the chemical space for
autonomous drug design
Randomized SMILES strings improve the quality of molecular generative models
Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. Herein we perform an extensive benchmark on models trained with subsets of GDB-13 of different sizes (1 million, 10,000 and 1000), with different SMILES variants (canonical, randomized and DeepSMILES), with two different recurrent cell types (LSTM and GRU) and with different hyperparameter combinations. To guide the benchmarks new metrics were developed that define how well a model has generalized the training set. The generated chemical space is evaluated with respect to its uniformity, closedness and completeness. Results show that models that use LSTM cells trained with 1 million randomized SMILES, a non-unique molecular string representation, are able to generalize to larger chemical spaces than the other approaches and they represent more accurately the target chemical space. Specifically, a model was trained with randomized SMILES that was able to generate almost all molecules from GDB-13 with a quasi-uniform probability. Models trained with smaller samples show an even bigger improvement when trained with randomized SMILES models. Additionally, models were trained on molecules obtained from ChEMBL and illustrate again that training with randomized SMILES lead to models having a better representation of the drug-like chemical space. Namely, the model trained with randomized SMILES was able to generate at least double the amount of unique molecules with the same distribution of properties comparing to one trained with canonical SMILES
Annotated chemical patent corpus: A gold standard for text mining
Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, t
Design and synthesis of soluble and cell-permeable PI3Kδ inhibitors for long-acting inhaled administration
PI3Kδ is a lipid kinase that is believed to be important in the migration and activation of cells of the immune system. Inhibition is hypothesised to provide a powerful yet selective immunomodulatory effect that may be beneficial for the treatment of conditions such as asthma or rheumatoid arthritis. In this work we describe the identification of inhibitors based on a thiazolopyridone core structure and their subsequent optimisation for inhalation. The initially identified compound (13) had good potency and isoform selectivity but was not suitable for inhalation. Addition of basic substituents to a region of the molecule pointing to solvent was tolerated (enzyme inhibition pIC50 >9) and by careful manipulation of the pKa and lipophilicity we were able to discover compounds (20b, 20f) with good lung retention and cell potency that could be taken forward to in-vivo studies where significant target engagement could be demonstrated
Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations
Molecular matched pair (MMP) analysis has been used for more than 40 years within molecular design and is still an important tool to analyse potency data and other compound properties. The methods used to find matched pairs range from manual inspection, through supervised methods to unsupervised methods, which are able to find previously unknown molecular pairs. Recent publications demonstrate the value of automatic MMP analysis of publicly available bioactivity databases. The MMP concept has its limitations, but because of its easy to use and intuitive nature, it will remain one of the most important tools in the toolbox of many drug designers
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