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Drug Discovery Researcher / リサーチャー(創薬)

LocationChiyoda City Tokyo Japan
RemoteSee job post for details
First listedIn the last 14 months

Job Description

Preferred Networks (PFN)のDrug Discovery領域を担うリサーチャー/エンジニアを募集します。

創薬分野における数値計算技術の発展はめざましく、医薬候補物質の設計・探索・最適化、生物活性・毒性・動態予測、合成経路探索、等の分野において計算化学技術の応用・実用化が検討されています。PFNのDrug Discoveryチームでは計算化学と創薬分野におけるドメイン知識を高度に融合して、製薬企業の医薬候補物質の設計・探索・最適化の支援に特に注力しております。


Drug Discoveryチームのミッション

  • 最先端の計算化学を活用した革新的医薬品の創出により、人々の健康に貢献すること

  • 創薬のための、医薬化学・計算化学に基づく革新的な技術の開発


  • 創薬の分子設計に関する研究開発

  • 深層学習と医薬化学の知識を活かし、実用的な化合物生成モデルの開発を目指した検討・開発を実施する。

  • 機械学習と物理化学シミュレーションを融合し、新たな結合評価技術を開発する。

  • 化合物生成モデルと結合評価方法を組み合わせた分子設計パイプラインを開発する。

  • 多数の生成化合物の解析・可視化ツールを開発する。

  • 他社との創薬分子設計の共同研究

  • 顧客課題をPFNの分子設計技術と計算資源を活用して解決する。
    - 顧客課題を聞き取り、医薬化学・構造生物学・計算化学的な視点から解決方法を考えて提案する。
    - 革新的な医薬品開発のための技術開発をし、それに基づいた顧客課題の解決をする。
    - 定期的な顧客への報告を実施し、顧客のフィードバックをもらいモデルを改善する。

  • 革新的な研究と現実への適用

  • 最新の研究・論文を把握し、熱意をもって革新的な技術開発を提案・実行する。

  • 大学・企業との共同研究を通して、世の中にない新たな技術を開発し、それを現実の医薬品開発に活かす。

  • 他チームとの連携・社内での技術交流を実施。様々な分野の最新技術を把握する。


Drug Discovery HP:https://projects.preferred.jp/drug-discovery/





We are recruiting researchers and engineers for the drug discovery field.

Computational techniques in medicinal chemistry have made remarkable progress, and practical application of technologies in computational chemistry are being considered in the fields of design, exploration, and optimization of drug candidates, prediction of biological activity, toxicity, pharmacokinetics, and synthetic pathway prediction.PFN's Drug Discovery team is particularly focusing on helping pharmaceutical companies identify, design, and optimize drug candidates through a sophisticated combination of domain knowledge in computational chemistry and drug discovery.

Our goal is to contribute to human health by creating innovative drugs using cutting-edge computational chemistry. We are looking for people who want to contribute to solving issues in drug discovery and development, where the difficulty and necessary development costs continue to increase year by year, and who want to be involved in the development of new drugs together with pharmaceutical companies.

We welcome applications from those who have knowledge and experience in related fields, as well as those who share our goals and enthusiasm.

Drug Discovery Team Mission

  • Contribute to human health through the creation of innovative medicines using cutting-edge computational chemistry

  • Develop innovative technologies based on medicinal and computational chemistry for drug discovery

Specific duties are expected to include the following (Flexible depending on skills and scope of interest)

  • Research and development of Drug Discovery.

  • Utilize knowledge of deep learning and medicinal chemistry to develop practical molecular generative model

  • Combine machine learning and physicochemical simulation to develop novel protein-ligand binding evaluation techniques.

  • Develop a molecular design pipeline that combines molecular generative models and binding evaluation methods.

  • Develop analysis and visualization tools for a large number of generated compounds.

  • Joint collaborations for drug discovery molecular design with our customers

  • Solve customers’ problems by utilizing the molecular design technology and computational resources of PFN.
    - Listen to customer issues and propose solutions from medicinal chemistry, structural biology and computational chemistry perspectives.
    - Develop technologies for innovative drug discovery and solve customer issues based on them.
    - Report regularly to customers and receive customers’ feedback to improve models.

  • Innovative research and real-world applications

  • Keep abreast of the latest researches and scientific literatures, and propose and implement innovative technological developments with enthusiasm.

  • Develop new technologies that do not exist in the world and apply them to real-world drug discovery and development through joint research with universities and companies.

  • Collaborate with other teams and conduct technical information exchanges within PFN. Keep abreast of the latest technologies in various fields.

Related links for this position


Minimum Qualifications

  • 知識・意欲

  • 計算化学、医薬化学、構造生物学いずれかに対する基本的な知識や関心

  • 機械学習・深層学習についての基礎的な知識

  • 最新の論文の動向を追いかけ、理解・活用ができる能力

  • 常に新しいドメインの知識を学ぶ意欲

  • 違う分野、企業のエンジニアと積極的に議論ができること

  • 経験

  • チームでの課題解決の経験

  • コンピュータサイエンスの知識を活用した課題解決の経験

  • ソフトウェア開発経験 (Python, C, C++ 等)
    - コンピューターアーキテクチャーを理解し、ソフトウェアの実行効率や、計算量を意識したプログラムの作成が出来る

  • 次の分野のうちいくつかの分野での実践・実務経験
    - アプリケーション開発もしくは運用経験
    - クライアント/バックエンド
    - Web/Desktop
    - ライブラリの開発経験

  • Knowledge and motivation

  • Basic knowledge or interest in either computational chemistry, medicinal chemistry and structural biology

  • Basic knowledge of machine learning and deep learning

  • Ability to follow and implement the latest paper trends

  • Willingness to constantly learn new domain knowledge

  • Ability to actively engage in discussions with engineers from different fields and companies.

  • Experience

  • Experience solving problems in a team environment

  • Experience in solving problems using computer-science knowledge

  • Software development experience in Python, C, C++, or similar languages
    - Ability to write programs with an understanding of computer architecture and an awareness of software execution efficiency and computational load

  • Experience in one or more of the following areas
    - Application development or operation
    - Client / Backend,
    - Web / Desktop
    - Development of libraries

Preferred Qualifications


※You do not need to have all of the skills listed below. We expect you to have excellent expertise in any of them.

  • 創薬化学プロジェクトにおける計算化学の適用経験。例えば、量子化学計算、分子動力学法、ケモインフォマティクスなどの技法に対する深い知識や実装経験、使用経験

  • 量子化学計算、分子動力学法ソフトウェア、ケモインフォマティクスのソフトウェア、ライブラリに対する知識、開発経験、あるいは類似のソフトウェアの実装経験。例えば、GaussianやGROMACS、NAMD、GENESIS、RDKitなど

  • 製薬企業や化学企業における実務経験

  • 計算化学、医薬化学、構造生物学分野、例えば物理化学シミュレーションと機械学習の融合に関する研究分野の開発・論文執筆経験

  • 有機化学 / 医薬化学 / 構造生物学 のいずれかについての知識

  • 機械学習・深層学習関連の研究・開発経験

  • プログラミング競技コンテスト、ゲームAIコンテスト、データ分析コンテスト(Kaggleなど)などの実績・経験。数理最適化、探索、数値解析などの知識・実装・使用経験。

  • 基本的なコンピュータサイエンスに関する知識

  • Unix/Linuxサーバ運用経験

  • Experience in the application of computational chemistry in drug discovery projects. For example, in-depth knowledge of and experience implementing and using techniques such as quantum chemical calculations, molecular dynamics methods, and cheminformatics

  • Knowledge and/or experience of developing quantum chemical, molecular dynamics, and chemoinformatics software and libraries,such as, Gaussian, GROMACS, NAMD, GENESIS, RDKit, etc.

  • Work experience in a pharmaceutical or chemical company.

  • Experience in research, development and writing papers in the field of computational chemistry, medicinal chemistry, structural biology (e.g., combining physical chemistry simulation and machine learning.)

  • Knowledge of organic chemistry, medicinal chemistry or structural biology

  • Machine learning and deep learning related research and development experience

  • Achievements/experience in programming competitions, game AI contests, data analysis contests (Kaggle, etc.), etc. Knowledge, implementation, and experience using mathematical optimization, search, and numerical analysis.

  • Knowledge of basic computer science

  • Experience in Unix/Linux server operation

Required documents in addition to a resume

  • 論文リスト
  • Paper list


  • 計算化学の力で、既存の新薬開発プロセスに革新的インパクトを与えることを目指しています
  • 各分野の最先端を研究している社内の技術者に気軽に質問・相談が出来る環境。
  • 難しい課題・挑戦的な課題にも果敢に取り組むことが推奨されている環境。
  • 潤沢な計算資源を利用した研究開発ができる。
  • We aim to create a revolutionary impact on the existing drug development process through the power of computational chemistry.
  • An environment where you can easily ask questions and consult with in-house engineers who are researching the cutting edge of their respective fields.
  • An environment where people are encouraged to boldly tackle difficult and challenging issues.
  • We are able to utilize our ample computational resources for research and development


Type of Employment

  • Full-time regular employment
  • Probation period: 3 months (under the same condition as regular employment)


  • Otemachi Bldg., 1-6-1 Otemachi, Chiyoda-ku, Tokyo

Work System

  • Five-day workweek (Saturdays and Sundays off), public holidays, New Year’s holiday
  • Discretionary-work (deemed work hours: 8 hours) or Flex-time system
  • Annual paid leave based on company regulations


  • Experience, performance, skills, contribution are taken into consideration.
  • Periodic assessment (2 times a year)
  • In addition to basic salary, bonuses are paid based on company’s performance and individual contribution. (Twice a year: Apr/Oct)
  • Commutation expenses


  • Various social insurance programs: pension insurance, health insurance, employment insurance, workers’ compensation
  • Vacation: Paid leave, maternity leave, parental leave, congratulation or condolence leave
  • Regular health checks
  • Allowance for purchasing a laptop PC.
  • Defined contribution pension


  • 正社員試用期間3ヶ月(本採用と同条件)


  • 東京都千代田区大手町1-6-1大手町ビル


  • 土曜日、日曜日、国民の祝日、国民の休日、 その他(慶弔、年末年始)
  • 専門労働型裁量労働制(みなし労働時間:8時間)もしくはフレックス制
  • 当社規定による年次有給休暇制度


  • 経験、業績、能力、貢献に応じて、当社規定により優遇
  • 年2回見直し
  • 基本給に加え、会社業績および個人のパフォーマンスに応じたボーナス(年2回、4月/10月)
  • 交通費支給


  • 社会保険完備(厚生年金保険、健康保険、雇用保険、労災保険)
  • 有給休暇、産前産後休暇、育児休暇、慶弔休暇等
  • 定期健康診断実施
  • ラップトップPC購入補助
  • 確定拠出年金制度

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