Newly Developed Semi-Solid Formulations Containing Mellilotus officinalis Extract: Characterization, Assessment of Stability, Safety, and Anti-Inflammatory Activity
Development of Glecaprevir: Conformations, Crystal Structures, and Efficient Solid–Solid Conversion for a Highly Polymorphic Macrocyclic Drug
Evaluating the Binding Potential and Stability of Drug-like Compounds with the Monkeypox Virus VP39 Protein Using Molecular Dynamics Simulations and Free Energy Analysis
Ciprofloxacin and Azithromycin Antibiotics Interactions with Bilayer Ionic Surfactants: A Molecular Dynamics Study
Modelling of Prednisolone Drug Encapsulation in Poly Lactic-co-Glycolic Acid Polymer Carrier Using Molecular Dynamics Simulations
Predicting Drug-Polymer Compatibility in Amorphous Solid Dispersions by MD Simulation: On the Trap of Solvation Free Energie
Elucidation of the sweetening mechanism of sweet orange fruit aroma compounds on sucrose solution using sensory evaluation, electronic tongue, molecular docking, and molecular dynamics simulation
Schrödinger デジタル創薬セミナー: Into the Clinic ~計算化学がもたらす創薬プロセスの変貌~ 第15回
JAN 16, 2025
Schrödinger デジタル創薬セミナー 15:
Water matters: Enhancing early drug discovery with insights from water energetics
水のエネルギー特性の理解は、新たな治療薬設計の戦略を提供します。タンパク質結合部位の水分子は分子認識やリガンド結合に影響を与える重要な要素であり、ヒット発見やリード最適化に活用することで、結合結果の予測精度を向上させ、新たな最適化機会を発見できます。
本ウェビナーでは、結合ポケット内の水エネルギーを活用する技術「WaterMap」と「Glide WS」を紹介します。WaterMapは不安定な水和サイトを特定し、より優れたヒットやリードを見つけ出し、Glide WSはその解析を活用して脱溶媒和の影響を評価し、正確なドッキングを実現します。これらの技術を組み合わせることで、仮想スクリーニングの初期段階でのリード選別精度が向上します。
実際、シュレーディンガー社のSARS-CoV-2プロジェクトでは、これらの技術を活用して、強力な酵素・細胞活性を持つリード化合物の迅速な発見を実現しました。
Webinar Highlights
- タンパク質結合部位における水分子の役割とリガンドドッキング結果への影響の概要
- 明示的な水のエネルギー特性をモデル化することで、新しい結合様式を解明し、誤陽性を減らす方法
- 水和パターンを活用して選択性や薬剤様特性を向上させるリード最適化の洞察
- シュレーディンガー社の薬剤発見プロジェクトにおける成功事例
Our Speakers
Abba Leffler
Senior Principal Scientist, Therapeutics Group, Schrödinger
プリンストン大学で化学学士号を取得し、ニューヨーク大学メディカルスクールで神経科学の博士号を取得しました。 彼の研究成果はScience、The Journal of Neuroscience、JCIM、PNASなどに掲載されており、第 I 相臨床試験中の化合物を含む、複数の特許を取得しています。 現在は、主任科学者として、低分子創薬プロジェクトをリードしています。
Gary Zhang
Product Manager, Docking Technologies, Schrödinger
デューク大学で生物システムにおける電荷移動経路の設計に関する研究で博士号を取得し、その後、スクリプス研究所でペプチドドッキング性能の向上に取り組むポスドク研修を行いました。現在は、ドッキング技術のプロダクトマネージャーとして、GlideやWScoreを含むSchrödingerのドッキングツールの性能向上と適用範囲の拡大を目指すチームを率いています。
【1月29日(水)～31日(金)】nano tech 2025 出展
Conference
【1月29日(水)～31日(金)】nano tech 2025 出展
Date & Time - January 29th-31st, 2025
Location - Tokyo, Japan
分子シミュレーション技術の発展により、ナノレベルの現象をコンピュータ・シミュレーションで扱うことができるようになってきました。また、シミュレーションと機械学習の組み合わせによってできることの幅が広がりつつあります。
シュレーディンガーは最新の分子シミュレーションや機械学習、その両者を融合した技術を使って、お客様の材料開発における解析力の強化と高効率化を支援します。
展示ブースでは、先進の技術について専門技術者が解説し、お客様からのご質問にお答えします。
※会期中、弊社展示ブースにてセミナーを開催いたします。
テーマ: 『原子レベルのシミュレーションと機械学習による材料開発』
様々な材料の物性値予測をコンピュータ上で可能に。事例をご紹介します。
さらに、各ソフトウェアを実機体験いただけます。ぜひお立ち寄りください。
【展示会情報】
展示会名：nano tech (国際ナノテクノロジー 総合展･技術会議)
会期：2025年1月29日(水) – 31日(金)
会場：東京ビッグサイト 東4ホール
小間番号：4M-10
出展ゾーン：材料：Materialゾーン
Future Food-Tech 2025
Conference
Future Food-Tech 2025
Date & Time - March 13th-14th, 2025
Location - San Francisco, California
Schrödinger is excited to be participating in the Future Food-Tech 2025 conference taking place on March 13th – 14th in San Francisco, California. Join us for a roundtable discussion hosted by Jeffrey Sanders, Product Manager and Technical Lead at Schrödinger, titled “From hype to results: Practical applications of modeling & AI advancing food product development.” Stop by our booth to speak with Schrödinger scientists.
MAR 13 | 3:00PM From hype to results: Practical applications of modeling & AI advancing food product development
Speaker:
Jeffrey Sanders, Product Manager and Technical Lead, Schrödinger
Abstract:
As AI has become a focus for major food and beverage companies, the shift from hype to results is occurring. In this round table we will discuss the challenges to leveraging internal data to build useful AI models for areas from ingredient selection to food formulation, and how to deal with data scarcity.
Device Packaging 2025
Conference
Device Packaging 2025
Date & Time - March 3rd-6th, 2025
Location - Phoenix, Arizona
Schrödinger is excited to be participating in the Device Packaging 2025 conference taking place on March 3rd – 6th in Phoenix, Arizona. Join our poster and collaborated talk with Samsung. Stop by booth #704 to speak with us.
MAR 5 | 5:30 PM Poster: Materials innovation for advanced electronic packaging using digital chemistry
Speaker:
Atif Afzal, Principal Scientist II, Schrödinger
Abstract:
The push for ever-improving characteristics of electronic devices demands packaging materials with superior thermal stability, mechanical strength, water repellency, and interfacial properties. Traditional material selection methods, often reliant on extensive empirical testing, are time-consuming and costly, limiting the ability for researchers to push beyond what they already know. To address these challenges, we propose a new approach that integrates physics-based modeling with machine learning (ML) to accurately model and predict the properties of advanced materials for electronic packaging. Our physics-based modeling, molecular dynamics (MD) simulations, offer detailed atomistic insights into material behavior under various conditions, providing essential data on thermal properties, mechanical resilience, adhesion, and more. To accelerate the material evaluation process and to navigate new chemical domains more efficiently, we integrate ML in our workflows. By training ML models using both experiment and simulation data, we can rapidly predict the properties of new materials, enabling efficient screening and selection.
We demonstrate the efficacy of this approach through a case study focused on designing copolymers with targeted properties. Our integrated MD-ML framework allows us to quickly identify polymers that meet specific performance criteria, such as enhanced glass transition and superior dielectric properties, while significantly reducing the time and resources required for material discovery. This work highlights the transformative potential of combining physics-based simulations with machine learning in the field of electronic packaging. By streamlining the material development process, our approach not only accelerates innovation but also enables the creation of materials that meet the stringent demands of next-generation electronic devices.
MAR 5 | 2:00 PM Talk: Material property simulation for advanced packaging
Speaker:
Seo Young, Samsung;
Atif Afzal, Schrödinger
Abstract:
Advanced packaging allows chiplet integration and maximizes device performance with faster product development cycle, lower cost, and higher yield. As the package size becomes bigger and the device is getting more complicated, there is growing motivation to employ manufacturing process simulation, Artificial Intelligence (AI) assisted process optimization, yield and reliability prediction, rather than conventional methods, to ramp the yield and to ensure the reliability of a new product. The key for an accurate process simulation model is to input precise material properties, such as modulus, Coefficient of Thermal Expansion (CTE), dielectric constant, glass transition temperature, etc., which could change non-linearly with temperature, moisture, as well as other environmental factors and process conditions. Molecular modeling and molecular dynamics can provide insights into post chemical reactions or physical transformations via atomic and molecular simulations.
Lithography Techniques for Redistribution Layer (RDL) fabrication are the foundation of Advanced Packaging techniques, such as Fan Out Wafer Level Packaging (FOWLP), Fan Out Panel Level Packaging (FOPLP), 2.5D, 3D, and 3.5D packaging with RDL interposers. The continuous scaling-down of critical dimensions (CDs) in advanced packages, including via diameters, routing line and space (L/S), to a few microns, or submicron level, as well as the increasing number of RDL layers at panel scale pose significant challenges in RDL lithography techniques. For example, the Photo Imageable Dielectric (PID) or other build-up dielectric materials used in multilayer RDL fabrication are polymers, having low Young’s modulus, high CTE, and big volume shrinkage after curing. These material properties could cause fabrication process induced warpage and surface topography deformations, such as non-planarity, roughness, contamination, defects, and dimensional variations, which could potentially lead to massive yield loss when forming fine features during the multilayer RDL patterning.
This paper presents material simulation methodologies based on quantum mechanics (QM), molecular dynamics (MD), and Machine Learning (ML), which are adopted to predict the material properties of a PID material, including glass transition temperature (Tg), CTE, mechanical properties, dielectric properties, as well as volume shrinkage after curing. Comparison between the simulation results and the experimental data is performed to validate the methodology. Similar methodology could be used to predict material properties of other organic packaging materials, which is crucial for building up accurate process, yield, and reliability simulation or digital twin of advanced packaging.