Tính không cộng gộp trong dữ liệu công khai và dữ liệu nội bộ: những tác động đối với thiết kế thuốc

Dea Gogishvili1, Eva Nittinger1, Christian Margreitter2, Christian Tyrchan1
1Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
2Computational Chemistry, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden

Tóm tắt

Tóm tắtNhiều dự án khám phá thuốc dựa trên ligand dựa vào phân tích mối quan hệ cấu trúc-hoạt tính (SAR), chẳng hạn như phân tích Free-Wilson (FW) hoặc phân tích cặp phân tử tương ứng (MMP). Về bản chất, chúng giả định tính tuyến tính và tính cộng gộp của các đóng góp của nhóm thay thế. Những kỹ thuật này gặp phải thách thức bởi tính không cộng gộp (NA) trong việc liên kết protein-ligand, nơi mà sự thay đổi của hai nhóm chức năng trong một phân tử dẫn đến hoạt tính cao hơn hoặc thấp hơn nhiều so với kỳ vọng từ các thay đổi đơn lẻ tương ứng. Việc xác định các trường hợp phi tuyến tính và các giải thích tiềm năng là rất quan trọng cho một dự án thiết kế thuốc vì nó có thể ảnh hưởng đến việc chọn hướng phát triển. Bằng cách phân tích có hệ thống tất cả dữ liệu hợp chất nội bộ của AstraZeneca (AZ) và dữ liệu sinh học ChEMBL25 công khai, chúng tôi cho thấy những sự kiện NA đáng kể trong gần như mỗi thử nghiệm thứ hai trong dữ liệu nội bộ và khoảng mỗi thử nghiệm thứ ba trong các bộ dữ liệu công khai. Hơn nữa, 9,4% tổng số hợp chất trong cơ sở dữ liệu AZ và 5,1% từ các nguồn công khai cho thấy sự chuyển dịch cộng gộp quan trọng chỉ ra các đặc điểm SAR hoặc các lỗi đo lường cơ bản quan trọng. Việc sử dụng dữ liệu NA kết hợp với học máy cho thấy rằng dữ liệu không cộng gộp rất khó để dự đoán và ngay cả việc thêm dữ liệu không cộng gộp vào việc đào tạo cũng không dẫn đến sự gia tăng khả năng dự đoán. Tóm lại, phân tích NA nên được áp dụng một cách thường xuyên trong nhiều lĩnh vực của hóa học tính toán và có thể cải thiện thêm thiết kế thuốc hợp lý.

Từ khóa


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