PMCID stringclasses 24
values | Title stringclasses 24
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PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The aim of transfer learning is to learn general features on the bigger data set, which also might be useful for the second task in the smaller data regime. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To generate focused molecule libraries, we first train on a large, general set of molecules, then perform fine-tuning on a smaller set of specific molecules, and after that start the sampling procedure. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To verify whether the generated molecules are active on the desired targets, standard target prediction was employed. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Machine learning based target prediction aims to learn a classifier c: M → to decide whether a molecule m ∈ molecular descriptor space M is active or not against a target. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The molecules are split into actives and inactives using a threshold on a measure for the substance effectiveness. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | pIC50 = −log10(IC50) is one of the most widely used metrics for this purpose. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | IC50 is the half maximal inhibitory concentration, that is, the concentration of drug that is required to inhibit 50% of a biological target’s function in vitro. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To predict whether the generated molecules are active toward the biological target of interest, target prediction models (TPMs) were trained for all the tested targets (5-HT2A, Plasmodium falciparum and Staphylococcus aureus). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | We evaluated random forest, logistic regression, (deep) neural networks, and gradient boosting trees (GBT) as models with ECFP4 (extended connectivity fingerprint with a diameter of 4) as the molecular descriptor. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | We found that GBTs slightly outperformed all other models and used these as our virtual assay in all studies (AUC[5-HT2A] = 0.877, AUC[Staph. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | aur.] |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | = 0.916). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | ECFP4 fingerprints were generated with CDK version 1.5.13. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | scikit-learn, XGBoost, and Keras were used as the machine learning libraries. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | For 5-HT2A and Plasmodium, molecules are considered as active for the TPM if their IC50 reported in ChEMBL is <100 nM, which translates to a pIC50 > 7, whereas for Staphylococcus, we used pMIC > 3. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The chemical language model was trained on a SMILES file containing 1.4 million molecules from the ChEMBL database, which contains molecules and measured biological activity data. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The SMILES strings of the molecules were canonicalized (which means finding a unique representation that is the same for isomorphic molecular graphs) before training with the CDK chemoinformatics library, yielding a SMILES file that contained one molecule per line. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | It has to be noted that ChEMBL contains many peptides, natural products with complex scaffolds, Michael acceptors, benzoquinones, hydroxylamines, hydrazines, etc., |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | which is reflected in the generated structures (see below). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | This corresponds to 72 million individual characters, with a vocabulary size of 51 unique characters. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | 51 characters is only a subset of all SMILES symbols, since the molecules in ChEMBL do not contain many of the heavy elements. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | As we have to set the number of symbols as a hyperparameter during model construction, and the model can only learn the distribution over the symbols present in the training data, this implies that only molecules with these 51 SMILES symbols seen during training can be generated during sampling. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The 5-HT2A, the Plasmodium falciparum, and the Staphylococcus aureus data sets were also obtained from ChEMBL. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | As these molecules were intended to be used in the rediscovery studies, they were removed from the training data before fitting the chemical language model. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To evaluate the models for a test set T, and a set of molecules GN generated from the model by sampling, we report the ratio of reproduced molecules , and enrichment over random (EOR), which is defined as6where n = |GN ∩ T| is the number of reproduced molecules from T by sampling a set GN of |GN| = N molecules from the... |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Intuitively, EOR indicates how much better the fine-tuned models work when compared to the general model. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | In this work, we address two points: First, we want to generate large sets of diverse molecules for virtual screening campaigns. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Second, we want to generate smaller, focused libraries enriched with possibly active molecules for a specific target. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | For the first task, we can train a model on a large, general set of molecules to learn the SMILES grammar. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Sampling from this model would generate sets of diverse, but unfocused molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To address the second task, and to obtain novel active drug molecules for a target of interest, we perform transfer learning: We select a small set of known actives for that target and we refit our pretrained chemical language model with this small data set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | After each epoch, we sample from the model to generate novel actives. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Furthermore, we investigate if the model actually benefits from transfer learning, by comparing it to a model trained from scratch on the small sets without pretraining. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | We employed a recurrent neural network with three stacked LSTM layers, each with 1024 dimensions, and each one followed by a dropout layer, with a dropout ratio of 0.2, to regularize the neural network. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The model was trained until convergence, using a batch size of 128. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The RNN was unrolled for 64 steps. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | It had 21.3 × 10 parameters. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | During training, we sampled a few molecules from the model every 1000 minibatches to inspect progress. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Within a few 1000 steps, the model starts to output valid molecules (see Table 1). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To generate novel molecules, 50,000,000 SMILES symbols were sampled from the model symbol-by-symbol. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | This corresponded to 976,327 lines, from which 97.7% were valid molecules after parsing with the CDK toolkit. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Removing all molecules already seen during training yielded 864,880 structures. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | After filtering out duplicates, we obtained 847,955 novel molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | A few generated molecules were randomly selected and depicted in Figure 4. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The Supporting Information contains more structures. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The created structures are not just formally valid but also mostly chemically reasonable. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | A few randomly selected, generated molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Ad = Adamantyl. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | In order to check if the de novo compounds could be considered as valid starting points for a drug discovery program, we applied the internal AstraZeneca filters. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | At AstraZeneca, this flagging system is used to determine if a compound is suitable to be part of the high-throughput screening collection (if flagged as “core” or “backup”) or should be restricted for particular use (flagged as “undesirable” since it contains one or several unwanted substructures, e.g., undesired reac... |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The filters were applied to the generated set of 848 k molecules, and they flagged most of them, 640 k (75%), as either core or backup. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Since the same ratio (75%) of core and backup compounds has been observed for the ChEMBL collection, we therefore conclude that the algorithm generates preponderantly valid screening molecules and faithfully reproduces the distribution of the training data. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To determine whether the properties of the generated molecules match the properties of the training data from ChEMBL, we followed the procedure of Kolb: We computed several molecular properties, namely, molecular weight, BertzCT, the number of H-donors, H-acceptors, and rotatable bonds, logP, and total polar surface ar... |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Then, we performed dimensionality reduction to 2D with t-SNE (t-distributed stochastic neighbor embedding, a technique analogous to PCA), which is shown in Figure 5. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Both sets overlap almost completely, which indicates that the generated molecules very well recreate the properties of the training molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | t-SNE projection of 7 physicochemical descriptors of random molecules from ChEMBL (blue) and molecules generated with the neural network trained on ChEMBL (green), to two unitless dimensions. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The distributions of both sets overlap significantly. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Furthermore, we analyzed the Bemis–Murcko scaffolds of the training molecules and the sampled molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Bemis–Murcko scaffolds contain the ring systems of a molecule and the moieties that link these ring systems, while removing any side chains. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | They represent the scaffold, or “core” of a molecule, which series of drug molecules often have in common. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The number of common scaffolds in both sets divided by the union of all scaffolds in both sets (Jaccard index) is 0.12, which indicates that the language model does not just modify side chain substituents but also introduces modifications at the molecular core. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To generate novel ligands for the 5-HT2A receptor, we first selected all molecules with pIC50 > 7 which were tested on 5-HT2A from ChEMBL (732 molecules), and then fine-tuned our pretrained chemical language model on this set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | After each epoch, we sampled 100,000 chars, canonicalized the molecules, and removed any sampled molecules that were already contained in the training set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Following this, we evaluated the generated molecules of each round of retraining with our 5-HT2A target prediction model (TPM). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | In Figure 6, the ratio of molecules predicted to be active by the TPM after each round of fine-tuning is shown. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Before fine-tuning (corresponding to epoch 0), the model generates almost exclusively inactive molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Already after 4 epochs of fine-tuning the model produced a set in which 50% of the molecules are predicted to be active. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Epochs of fine-tuning vs ratio of actives. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | In order to assess the novelty of the de novo molecules generated with the fine-tuned model, a nearest neighbor similarity/diversity analysis has been conducted using a commonly used 2D fingerprint (ECFP4) based similarity method (Tanimoto index).Figure 7 shows the distribution of the nearest neighbor Tanimoto index ge... |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | For each bin, the white bars indicate the molecules generated from the unbiased, general model, while the darker bars indicate the molecules after several epochs of fine-tuning. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Within the bins corresponding to lower similarity, the number of molecules decreases, while the bins of higher similarity get populated with increasing numbers of molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The plot thus shows that the model starts to output more and more similar molecules to the target-specific training set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Notably, after a few rounds of training not only are highly similar molecules produced but also molecules covering the whole range of similarity, indicating that our method could deliver not only close analogues but also new chemotypes or scaffold ideas to a drug discovery project. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To have the best of both worlds, that is, diverse and focused molecules, we therefore suggest to sample after each epoch of retraining and not just after the final epoch. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Nearest-neighbor Tanimoto similarity distribution of the generated molecules for 5-HT2A after n epochs of fine-tuning against the known actives. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The generated molecules are distributed over the whole similarity range. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Generated molecules with a medium similarity can be interesting for scaffold-hopping. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Plasmodium falciparum is a parasite that causes the most dangerous form of malaria. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To probe our model on this important target, we used a more challenging validation strategy. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | We wanted to investigate whether the model could also propose the same molecules that medicinal chemists chose to evaluate in published studies. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To test this, first, the known actives against Plasmodium falciparum with a pIC50 > 8 were selected from ChEMBL (Table 2). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Then, this set was split randomly into a training (1239 molecules) and a test set (1240 molecules). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The chemical language model was then fine-tuned on the training set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | 7500 molecules were sampled after each of the 20 epochs of refitting. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | EOR: Enrichment over random. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | This yielded 128,256 unique molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Interestingly, we found that our model was able to “redesign” 28% of the unseen molecules of the test set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | In comparison to molecules sampled from the unspecific, untuned model, an enrichment over random (EOR) of 66.9 is obtained. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | With a smaller training set of 100 molecules, the model can still reproduce 7% of the test set, with an EOR of 19.0. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To test the reliance on pIC50 we chose to use another cutoff of pIC50 > 9, and took 100 molecules in the training set and 1022 in the test set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | 11% of the test set could be recreated, with an EOR of 35.7. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To visually explore how the model populates chemical space, Figure 8 shows a t-SNE plot of the ECFP4 fingerprints of the test molecules and 2000 generated molecules that were predicted to be active by the target prediction model for Plasmodium falciparum. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | It indicates that the model has generated many similar molecules around the test examples. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | t-SNE plot of the pIC50 > 9 test set (blue) and the de novo molecules predicted to be active (green). |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | The language model populates chemical space around the test molecules. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | To evaluate a different target, we furthermore conducted a series of experiments to reproduce known active molecules against Staphylococcus aureus. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | Here, we used actives with a pMIC > 3. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | MIC is the mean inhibitory concentration, the lowest concentration of a compound that prevents visible growth of a microorganism. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | As above, the actives were split into a training and a test set. |
PMC5785775 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. | However, here, the availability of the data allows larger test sets to be used. |
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