| Page 21 | Kisaco Research

Explore how AI and large language models are revolutionizing reaction prediction, retrosynthesis planning, and synthetic accessibility scoring.
Learn how to evaluate and optimize AI-generated leads for real-world developability, including solubility, stability, and synthetic tractability.

Author:

Ethan Pickering

Head, Data Science & ML Research
Bayer

Ethan Pickering

Head, Data Science & ML Research
Bayer

Explore how knowledge graphs integrate multi-source biological data, such as genetic, proteomic, and clinical information, into unified models that accelerate target discovery and disease understanding, with AI enhancing the extraction of actionable insights.
Learn how data normalization and the latest curation strategies ensure that biological datasets are clean, standardized, and AI-ready, enabling accurate analysis and improved model performance for drug development.

Author:

Daniyal Hussain

Executive Director, Technology Business Development
GSK

Daniyal Hussain

Executive Director, Technology Business Development
GSK

Author:

Michael Steinbaugh

Director, Data, AI & Genome Sciences
Merck

Michael Steinbaugh

Director, Data, AI & Genome Sciences
Merck

Author:

Shameer Khader

Executive Director, Precision Medicine & Computational Biology
Sanofi

Shameer Khader

Executive Director, Precision Medicine & Computational Biology
Sanofi

Hear cross-functional perspectives on successfully implementing AI across process development teams, from aligning with quality, IT, and manufacturing to overcoming cultural and technical barriers, with a focus on driving operational efficiency and long-term value.

Author:

Ramila Pieres

Global Head, Data Management, ML/AI, MSAT
Sanofi

Ramila Pieres

Global Head, Data Management, ML/AI, MSAT
Sanofi

Author:

Shruti Vij

Associate Director, Data Analytics & Modeling
Takeda

Shruti Vij

Associate Director, Data Analytics & Modeling
Takeda

Dive deep into how large language models are automating complex planning tasks, from trial feasibility assessments and synthetic protocol generation to cross-functional alignment and regulatory-ready documentation, with real-world examples of scalable implementation and measurable impact.

Explore how AI accelerates the design of complex biologics, including ADCs and engineered cell therapies.
Learn how predictive models improve developability by forecasting linker stability, payload efficacy, and manufacturability.

Author:

Monica Wang

Head, Biologics & Novel Modality Discovery Capabilities & Products, Scientific Informatics
Takeda

Monica Wang

Head, Biologics & Novel Modality Discovery Capabilities & Products, Scientific Informatics
Takeda

Author:

Yorgos Psarellis

Senior Computational & Machine Learning Scientist
Sanofi

Yorgos Psarellis

Senior Computational & Machine Learning Scientist
Sanofi

Explore how AI-driven digital twins and functional models integrate patient-specific biology to identify and validate high-confidence drug targets by simulating system-level responses to genetic or pharmacological perturbations.
Learn how perturbation modelling with multiomic and functional genomics data predicts the effects of interventions on disease pathways, while LLMs synthesize data to uncover and prioritize novel therapeutic targets.

Author:

Zhiyong (Sean) Xie

Vice President & Head, AI & Data Science
Xellarbio

Zhiyong (Sean) Xie

Vice President & Head, AI & Data Science
Xellarbio

Equip teams with AI tools that capture process knowledge and simulate scale-up scenarios, reducing tech transfer timelines and improving first-batch success rates - critical for aligning R&D, MSAT, and manufacturing expectations early.

Author:

Irfan Ali Mohammed

Director, CMC
Alexion Pharmaceuticals

Irfan Ali Mohammed

Director, CMC
Alexion Pharmaceuticals

Gain actionable strategies for embedding generative AI and large language models into early-phase trial design and execution, from protocol drafting and site selection to patient engagement, accelerating timelines while ensuring data quality and compliance