Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154448/1/sim8445_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154448/2/sim8445.pd

딥러닝 기반의 분자 특성 예측 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 협동과정 생물정보학전공, 2021.8. 윤성로.Deep learning (DL) has been advanced in various fields, such as vision tasks, language processing, and natural sciences. Recently, several remarkable researches in computational chemistry were accomplished by DL-based methods. However, the chemical system consists of diverse elements and their interactions. As a result, it is not trivial to predict chemical properties which are determined by intrinsically complicated factors. Consequently, conventional approaches usually depend on tremendous calculations for chemical simulations or predictions, which are cost-intensive and time-consuming. To address recent issues, we studied deep learning for computational chemistry. We focused on the chemical property prediction from molecular structure representations. A molecular structure is a complex of atoms and their arrangements. The molecular property is determined by the interactions from all these components. Therefore, molecular structural representations are the key factor in the chemical property prediction tasks. In particular, we explored public property prediction tasks in pharmacology, organic chemistry, and quantum chemistry. Molecular structures can be described as categorical sequences or geometric graphs. We utilized both representational formats for prediction tasks, and achieved competitive model performances. Our studies verified that the molecular representation is essential for various tasks in chemistry, and using appropriate types of neural networks for the representation type is significant to the model predictability.딥러닝 방법론은 이미지 및 언어 처리 분야를 포함하여, 공학 및 자연과학을 포함한 여러 분야에서 진보하였다. 최근에는 특히 계산 화학 분야에서 딥러닝 기반으로 연구된 우수한 성과들이 여럿 보고되었다. 그러나 화학적인 계 내에서는 많은 종류의 요소들과 상호작용들이 복잡하게 얽혀있다. 따라서 이러한 요소들을 이용하여 화학 특성을 예측하는 것은 쉽지 않은 일이다. 결과적으로, 전통적인 방법들은 주로 상당한 비용과 시간이 소요되는 엄청난 계산량을 기반으로 하였다. 이러한 한계점을 해결하기 위하여, 본 연구는 딥러닝을 활용한 화학에서의 계산 문제를 연구하였다. 본 연구에서는 특히 분자 구조 표현 데이터를 이용, 분자의 특성을 예측하는 문제들에 집중하였다. 분자 구조는 다양한 원자들이 특정한 배열을 이루고 있는 복합체이며, 분자 특성은 이러한 원자 및 그들의 상호 관계들에 의하여 결정 된다. 따라서, 분자 구조는 화학적 특성을 예측하는 문제에 있어서 필수적인 요소이다. 본 연구에서는 약학, 유기 화학, 양자 화학 등 다양한 분야에서의 화학 특성 예측연구들을 진행하였다. 분자 구조는 시퀀스 혹은 그래프 형태로 표현할 수 있고, 본 연구에서는 두 가지 형태를 모두 활용하여서 진행하였다. 본 연구는 분자 표현이 화학 분야 내의 여러 가지 태스크에 활용 될 수 있으며, 분자 표현에 따른 적절한 딥러닝 모델의 선택이 모델 성능을 크게 높일 수 있음을 보였다.1 Introduction 1 1.1 Motivation 1 1.2 Contents of dissertation 3 2 Background 8 2.1 Deep learning in Chemistry 8 2.2 Deep Learning for molecular property prediction 9 2.3 Approaches for molecular property prediction 12 2.3.1 Sequential modeling for molecular string 12 2.3.2 Structural modeling for molecular graph 15 2.4 Tasks on molecular properties 20 2.4.1 Pharmacological tasks 20 2.4.2 Biophysical and physiological tasks 21 2.4.3 Quantum-mechanical tasks 21 3 Application I. Drug class classification 23 3.1 Introduction 23 3.2 Proposed method 26 3.2.1 Preprocessing 27 3.2.2 Model architecture 27 3.2.3 Training and evaluation 30 3.3 Experimental results 31 3.4 Discussion 37 4 Application II. Biophysical property prediction 39 4.1 Introduction 39 4.2 Proposed method 41 4.2.1 Preprocessing 41 4.2.2 model architecture 42 4.2.3 Training and evaluation 45 4.3 Experimental results 47 4.4 Discussion 53 5 Application III. Quantum-mechanical property prediction 55 5.1 Introduction 55 5.2 Proposed method 57 5.2.1 Preprocessing 59 5.2.2 Model architecture 62 5.2.3 Training and evaluation 67 5.3 Experimental results 69 5.4 Discussion 70 6 Conclusion 74 Bibliography 76 초 록 93박

Dynamics of myosin, microtubules, and Kinesin-6 at the cortex during cytokinesis in Drosophila S2 cells

© The Authors, 2009 . This article is distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License. The definitive version was published in Journal of Cell Biology 186 (2009): 727-738, doi:10.1083/jcb.200902083.Signals from the mitotic spindle during anaphase specify the location of the actomyosin contractile ring during cytokinesis, but the detailed mechanism remains unresolved. Here, we have imaged the dynamics of green fluorescent protein–tagged myosin filaments, microtubules, and Kinesin-6 (which carries activators of Rho guanosine triphosphatase) at the cell cortex using total internal reflection fluorescence microscopy in flattened Drosophila S2 cells. At anaphase onset, Kinesin-6 relocalizes to microtubule plus ends that grow toward the cortex, but refines its localization over time so that it concentrates on a subset of stable microtubules and along a diffuse cortical band at the equator. The pattern of Kinesin-6 localization closely resembles where new myosin filaments appear at the cortex by de novo assembly. While accumulating at the equator, myosin filaments disappear from the poles of the cell, a process that also requires Kinesin-6 as well as possibly other signals that emanate from the elongating spindle. These results suggest models for how Kinesin-6 might define the position of cortical myosin during cytokinesis.This work was supported by a National Institutes of Health grant NIH 38499 to R.D. Vale

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments