Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

https://pubs.rsc.org/en/content/articlepdf/2019/NP/C8NP00092A

Marine natural products.

A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019–2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)1–4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (Mpro) of SARS-CoV-2: Mpro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-25,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of Mpro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds—including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds—as inhibitors of Mpro. Six of these compounds inhibited Mpro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 μM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available. A programme of structure-assisted drug design and high-throughput screening identifies six compounds that inhibit the main protease of SARS-CoV-2, demonstrating the ability of this strategy to isolate drug leads with clinical potential.

/pdf/structure-of-mpro-from-sars-cov-2-and-discovery-of-its-2hyiph66s6.pdf

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

This report describes a number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens. The compounds identified by such substructural features are not recognized by filters commonly used to identify reactive compounds. Even though these substructural features were identified using only one assay detection technology, such compounds have been reported to be active from many different assays. In fact, these compounds are increasingly prevalent in the literature as potential starting points for further exploration, whereas they may not be.

/pdf/new-substructure-filters-for-removal-of-pan-assay-3e3dudtvn0.pdf

New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays

본 발명은 화합물의 결정형 A에 관한 것이다. 본 발명은 상기 결정형 A의 제조방법과 약물 조성물을 더 공개한다. 상기 결정형 A는 비교적 강한 체내 당 강하 활성을 구비하여 II형 당뇨병 및/또는 II형 당뇨병의 합병증을 치료 또는 예방하는 신규한 약물 활성 성분이 될 수 있다.

화합물의 결정형 a 및 이의 제조방법

The invention relates to the fields of medicinal chemistry and pharmacotherapeutics, in particular to compounds of a general formula (I), whichserve as an enterovirus protease inhibitor. The compounds have remarkable inhibition activity on coronavirus [SRAS (severe acute respiratory syndromes)] main proteinase and can be used for treating related diseases. The invention also relates to a preparation method of the compounds, a medicinal composition of the compounds, and medicinal salt, enantiomeric form and diastereoisomer as well as racemic compounds. (The formula I is as shown in the description).

Aldehyde group compounds, as well as preparation method and application thereof

MOTIVATION The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-consuming, especially for the exact determination of micro pKa information at the atomic level. Hence, a fast and accurate prediction of pKa values of chemical compounds is of broad interest. RESULTS Here, we compiled a large scale pKa dataset containing 16595 compounds with 17489 pKa values. Based on this dataset, a novel pK a prediction model, named Graph-pKa, was established using graph neural networks. Graph-pKa performed well on the prediction of macro pK a values, with a mean absolute error around 0.55 and a coefficient of determination around 0.92 on the test dataset. Furthermore, combining multi-instance learning, Graph-pKa was also able to automatically deconvolute the predicted macro pKa into discrete micro pK a values. AVAILABILITY The Graph-pK a model is now freely accessible via a web-based interface (https://pka.simm.ac.cn/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab714/41089809/btab714.pdf

Multi-instance learning of graph neural networks for aqueous pKa prediction.

Bexarotene (BEX) was approved by the FDA in 1999 for the treatment of cutaneous T-cell lymphoma (CTCL). The poor aqueous solubility causes the low bioavailability of the drug and thereby limits the clinical application. In this study, we developed a GCN-based deep learning model (CocrystalGCN) for in-silico screening of the cocrystals of BEX. The results show that our model obtained high performance relative to baseline models. The top 30 of 109 coformer candidates were scored by CocrystalGCN and then validated experimentally. Finally, cocrystals of BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate were successfully obtained. The crystal structures were determined by single-crystal X-ray diffraction. Powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis were utilized to characterize these multi-component forms. All cocrystals present superior solubility and dissolution over the parent drug. The pharmacokinetic studies show that the plasma exposures (AUC0−8h) of BEX-pyrazine and BEX-2,5-dimethylpyrazine are 1.7 and 1.8 times that of the commercially available BEX powder, respectively. This work sets a good example for integrating virtual prediction and experimental screening to discover the new cocrystals of water-insoluble drugs.

/pdf/cocrystal-prediction-of-bexarotene-by-graph-convolution-2fdwec3m.pdf

Kaixian Chen

Papers

화합물의 결정형 a 및 이의 제조방법

Aldehyde group compounds, as well as preparation method and application thereof

Multi-instance learning of graph neural networks for aqueous pKa prediction.

Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

Design, synthesis and biological evaluation of 2-aminopyridine derivatives as USP7 inhibitors.