Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can also help identify suitable molecules for disease treatment. In this paper, we propose Taiga, a transformer-based architecture for the generation of molecules with desired properties. Using a two-stage approach, we first treat the problem as a language modeling task of predicting the next token, using SMILES strings. Then, we use reinforcement learning to optimize molecular properties such as QED. This approach allows our model to learn the underlying rules of chemistry and more easily optimize for molecules with desired properties. Our evaluation of Taiga, which was performed with multiple datasets and tasks, shows that Taiga is comparable to, or even outperforms, state-of-the-art baselines for molecule optimization, with improvements in the QED ranging from 2 to over 20 percent. The improvement was demonstrated both on datasets containing lead molecules and random molecules. We also show that with its two stages, Taiga is capable of generating molecules with higher biological property scores than the same model without reinforcement learning.

Background Drug–drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug–drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug’s existing interactions, such an approach is unsuitable, and other drug’s preferences can be used to accurately predict new Drug–drug interactions. Methods We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs’ interactions and the drug’s biomedical text embeddings to predict the DDIs of both new and well known drugs. Results Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs’ biomedical prediction task by presenting text embedding’s contribution to a multi-modal pregnancy drug safety classification. Conclusion Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug–drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature.

The click-through rate (CTR) reflects the ratio of clicks on a specific item to its total number of views. It has significant impact on websites' advertising revenue. Learning sophisticated models to understand and predict user behavior is essential for maximizing the CTR in recommendation systems. Recent works have suggested new methods that replace the expensive and time-consuming feature engineering process with a variety of deep learning (DL) classifiers capable of capturing complicated patterns from raw data; these methods have shown significant improvement on the CTR prediction task. While DL techniques can learn intricate user behavior patterns, it relies on a vast amount of data and does not perform as well when there is a limited amount of data. We propose XDBoost, a new DL method for capturing complex patterns that requires just a limited amount of raw data. XDBoost is an iterative three-stage neural network model influenced by the traditional machine learning boosting mechanism. XDBoost's components operate sequentially similar to boosting; However, unlike conventional boosting, XDBoost does not sum the predictions generated by its components. Instead, it utilizes these predictions as new artificial features and enhances CTR prediction by retraining the model using these features. Comprehensive experiments conducted to illustrate the effectiveness of XDBoost on two datasets demonstrated its ability to outperform existing state-of-the-art (SOTA) models for CTR prediction.