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日程: 2006年9月14日 (木) 14:00~
場所: 産業技術総合研究所 臨海副都心センター別館11階第1会議室

演者:Dr. James Galagan(Broad Institute&s_comma; USA)&s_comma;
Dr. Sung Kwang Lee(Bioinformatics & Molecular Design Research Center&s_comma; Korea)

Dr. James Galaganは、Broad Instituteの微生物ゲノム解析のAssocite Directorで、Neurospora crassa(赤パンカビ)のゲノム論文(Nature)の
筆頭著者として有名です。産総研CBRCも参加した、麹菌(Aspergillus oryzae)、A. nidulans、A. fumitatusの3種ゲノム比較研究の国際プロジェクトの推進者の一人で、A. nidulansのゲノム論文(Nature)の筆頭著者でもあります。

The last decade has witnessed a revolution in the genomics of the fungal kingdom.
Over 40 complete fungal genomes have been publicly released with an equal number currently being sequenced - representing the widest sampling of genomes from any eukaryotic kingdom.

These data provide an unparalleled opportunity to study the biology and evolution of medically&s_comma; industrially&s_comma; and environmentally important fungi.
Fungi also serve as model organisms for all eukaryotes&s_comma; and the available fungal genomic resource&s_comma; coupled&s_comma; with the experimental tractability of the fungi&s_comma; is thus accelerating research into fundamental aspects of eukaryotic biology.

To fully take advantage of these data requires developing new bioinformatics tools and methods&s_comma; especially for comparative analysis.
I will provide an update on the status of fungal sequencing at the Broad Institute.
I will also describe recent advances in generating high quality genome annotation.
I will then review some of the scientific advances and discoveries fungal genome sequences have driven or enabled&s_comma; focusing in particular on gene structure&s_comma; genome evolution&s_comma; gene regulation and the methods we have developed to study these topics.

韓国BMD研究所の Sung Kwang Lee博士は、同所長のKyoung Tai No教授とともに薬物の体内での吸収・分散・代謝・排出等の数理モデルを研究し"preADMET"というパッケージソフトを製作してきた責任者です。同研究所では、多くの薬剤候補化合物を発見してきましたが、同ソフトを化合物探索の初期段階に組み込むことで、副作用の回避が狙えます。
今回は、特定非営利活動法人 並列生物情報処理イニシアティブで(http://www.ipab.org/)のご講演での来日にあわせて産業技術総合研究所でのご講演をお願い致しました。

Title In silico ADME/Tox prediction for chemical library: The PreADMET program

PreADMET is a web-based application for predicting ADME data and building drug-like library using in silico method. PreADME ver 1.0
is also commercially available and ver 2.0 release is scheduled.
A significant bottleneck remains in the drug discovery procedure&s_comma;in particular in the later stages of lead discovery&s_comma; is analysis of
the ADME and overt toxicity properties of drug candidates. Over 50% of the candidates failed due to ADME/Tox deficiencies during development.
To avoid this failure at the development a set of in vitro ADME/Tox screens has been implemented in most pharmaceutical companies with the aim of discarding compounds in the discovery phase that are likely to fail further down the line. Even though the early stage in vitro ADME reduces the probability of the failure at the development stage&s_comma;it is still time-consuming and resource-intensive.
Therefore&s_comma; we describe a new web-based application called PreADMET&s_comma; which has been developed in response to a need for rapid prediction of drug-likeness and ADME/Tox data.
関嶋 政和 / SEKIJIMA Masakazu&s_comma; Ph.D.
Mail: m.sekijima@aist.go.jp / Web: http://www.cbrc.jp/~sekijima/index_j.html
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