Booth建议创新型生物技术公司采取更保守的披露策略，如延后专利申请、减少发表，以防AI驱动的模仿。
显然，在AI（以及中国）的新现实下，需要从根本上改变信息披露规范：更晚申请专利、更多使用诱饵申请、尽量减少公关、早期项目少做会议海报、限制科学出版物的数量和深度等。原文 (en)
it seems clear that one needs to fundamentally change the disclosure norms in this new AI (and China) reality: file patents later, use more decoy filings, keep PR to a minimum, fewer conference posters for early programs, limit the number and depth of scientific publications, etc….
Bruce Booth预测，未来3-5年AI将带来药物过剩浪潮，先恶化行业整体研发效率，之后才会改善。
但我的预测是：未来3-5年，AI将带来一波药物过剩，加剧行业整体研发效率的难题，而不是改善这些指标。重大的新技术浪潮往往先让事情变糟，然后才会变好。原文 (en)
But here’s my prediction: AI will drive a wave of drug abundance and exacerbate the industry’s aggregate R&D productivity challenges over the next 3-5 years, rather than improve those metrics. Great new technology waves often make things worse before they make them better.
Booth认为当前AI主要在已知靶点和骨架上普及设计能力，训练集之外预测能力很差。
但关键在于，当今的AI主要是在已知靶点、已知药物骨架和已知生物学周围实现设计的民主化。大多数AI模型的训练集涉及“已知”的药物发现领域，在这些数据集之外的预测能力很差。原文 (en)
But, critically, AI today is predominantly democratizing design around known targets, known drug scaffolds, and known biology. The training sets for most AI models involve the “known” drug discovery landscape, and predictive ability outside of those sets is poor
Booth认为AI将加剧热门靶点的扎堆与同质化竞争。
鉴于此，我相信AI必将加剧行业的扎堆问题——针对前八大热门靶点，全球已有超过100个研发项目激烈竞争。原文 (en)
In light of this, I believe AI will undoubtedly exacerbate the crowding challenge in our sector – that intense competition that has brought over 100 global programs into R&D against each of the top eight targets
Booth指出AI特别擅长绕开专利，设计出略有差异的新分子。
事实上，破解专利是AI的强项之一：挖掘专利申请，识别可能的“先导”药物结构或序列，找出专利的薄弱点，设计出略有不同且可能更好的新分子。原文 (en)
Indeed, patent-busting is one of AI’s strong suits: mine a patent application, identify what is likely the “lead” drug structure or sequence, and figure out where the soft spots in the patent are to make new molecules, incrementally different and potentially better than the patented version.
Booth怀疑AI在临床开发阶段只能带来10-20%的增量改善。
我对此有些怀疑：行业开发流程中固有的低效和延迟（如“在体”治疗周期）能为AI带来的改进空间，不会超过10-20%的增量。原文 (en)
I’m somewhat skeptical that the embedded inefficiencies and latencies of the industry’s Development machine (like “in-life” treatment periods) offer more than incremental 10-20% improvement opportunities for AI impact.