Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.

Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein.

Chemical-gene relation extraction using recursive neural network.

In this article, we describe our system for the CHEMPROT task of the BioCreative VI challenge. Although considerable research on the named entity recognition of genes and drugs has been conducted, there is limited research on extracting relationships between them. Extracting relations between chemical compounds and genes from the literature is an important element in pharmacological and clinical research. The CHEMPROT task of BioCreative VI aims to promote the development of text mining systems that can be used to automatically extract relationships between chemical compounds and genes. We tested three recursive neural network approaches to improve the performance of relation extraction. In the BioCreative VI challenge, we developed a tree-Long Short-Term Memory networks (tree-LSTM) model with several additional features including a position feature and a subtree containment feature, and we also applied an ensemble method. After the challenge, we applied additional pre-processing steps to the tree-LSTM model, and we tested the performance of another recursive neural network model called Stack-augmented Parser Interpreter Neural Network (SPINN). Our tree-LSTM model achieved an F-score of 58.53% in the BioCreative VI challenge. Our tree-LSTM model with additional pre-processing and the SPINN model obtained F-scores of 63.7 and 64.1%, respectively.Database URL: https://github.com/arwhirang/recursive_chemprot.

Drug drug interaction extraction from the literature using a recursive neural network.

Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge'13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models.

BEST: Next-Generation Biomedical Entity Search Tool for Knowledge Discovery from Biomedical Literature.

As the volume of publications rapidly increases, searching for relevant information from the literature becomes more challenging. To complement standard search engines such as PubMed, it is desirable to have an advanced search tool that directly returns relevant biomedical entities such as targets, drugs, and mutations rather than a long list of articles. Some existing tools submit a query to PubMed and process retrieved abstracts to extract information at query time, resulting in a slow response time and limited coverage of only a fraction of the PubMed corpus. Other tools preprocess the PubMed corpus to speed up the response time; however, they are not constantly updated, and thus produce outdated results. Further, most existing tools cannot process sophisticated queries such as searches for mutations that co-occur with query terms in the literature. To address these problems, we introduce BEST, a biomedical entity search tool. BEST returns, as a result, a list of 10 different types of biomedical entities including genes, diseases, drugs, targets, transcription factors, miRNAs, and mutations that are relevant to a user's query. To the best of our knowledge, BEST is the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results. BEST is freely accessible at http://best.korea.ac.kr.

Minimally invasive anterior lumbar interbody fusion followed by percutaneous pedicle screw fixation for isthmic spondylolisthesis.

BACKGROUND CONTEXT: Most surgeons have thought that posterior decompression is necessary to treat isthmic spondylolisthesis with leg pain. However, the surgical procedure not only requires wide muscle dissection but can also lead to spinal instability. The authors' treatment concept for isthmic spondylolisthesis is one-stage anterior reduction and posterior stabilization with minimally invasive surgical procedure without touching the spinal thecal sac and nerve. PURPOSE: To investigate a new surgical concept of minimally invasive anterior-posterior fusion without posterior decompression for the treatment of isthmic spondylolisthesis with leg pain. STUDY DESIGN: This is a retrospective study of 73 patients with isthmic spondylolisthesis who underwent minimally invasive anterior lumbar interbody fusion (mini-ALIF) followed by percutaneous pedicle screw fixation (PF) between October 2000 and February 2002. PATIENT SAMPLE: A total of 73 patients with low-grade isthmic spondylolisthesis (46 with Grade 1 and 27 with Grade 2) who underwent mini-ALIF followed by percutaneous PF were retrospectively analyzed. There were 20 men and 53 women, with a mean age of 50.6 (range, 19 to 77) years. All patients had low back pain and referred or radicular leg pain or neurogenic intermittent claudication in walking or standing. Average duration of symptoms was 26 (range, 3 to 120) months. OUTCOME MEASURES: The clinical outcome was graded according to the modified Macnab criteria. METHODS: The authors retrospectively reviewed clinical and radiological data of 73 patients who had isthmic spondylolisthesis. All patients underwent mini-ALIF and percutaneous PF on the same day between October 2000 and February 2002. The mean follow-up period was 16 months (range, 12 to 26). RESULTS: The mean operating time, blood loss and hospital stay were 210 minutes, 135 ml and 4.1 days, respectively. No blood transfusion was necessary. Clinical outcome was excellent in 26 patients (35.6%), good in 43 (58.9%), fair in 3 (4.1%) and poor in 1 (1.4%). The fusion rate was 97.3% (71 of 73). There were 6 cases (8.2%) of mini-ALIF complications and 6 (8.2%) of percutaneous PF complications. There were no postoperative neurologic deficits. CONCLUSIONS: Mini-ALIF followed by percutaneous PF is an efficacious alternative for low-grade isthmic spondylolisthesis, and posterior decompression is not necessary to relieve leg symptoms. This minimally invasive combined procedure offers many advantages, such as preservation of posterior arch, no nerve retraction, less blood loss, excellent cosmetic results, high fusion rate and early discharge.