PharmaResearch_Priya OP
Medicinal Chemist · Big Pharma · 9 yrs
Everyone's talking about AlphaFold changing drug discovery and yes it did solve protein structure prediction in a way that took decades off certain research paths. But I want a real discussion — are we actually seeing faster drug pipelines or are we just predicting more structures that then still fail in preclinical trials? At my company we've used generative AI for molecule design and shortlisted candidates 60% faster. But attrition rate in Phase 1 hasnt improved yet. Thoughts?
AidanSci_Biotech
Computational Biologist · Biotech startup
You nailed it — AI in pharma R&D is compressing the target identification and lead optimization phases massively. Traditional timeline was 4-6 yrs to get a candidate to IND (Investigational New Drug) filing. With AI-assisted workflows some companies report getting there in 18-24 months. Insilico Medicine got a de novo AI-designed drug into Phase 2 trials in 2023 — that's genuinely historic. But youre right that Phase 1-3 failure rates havent budged much. The biology is still the hard part, not the chemistry.
MedFinance_Feroz
Healthcare Economist · Policy research
The big question nobody's answering: will AI-reduced R&D costs actually lower drug prices for patients? Spoiler — probably not much. Pharma pricing is driven by market exclusivity and patent strategy, not R&D cost. If AI cuts development cost by 40% the savings go to shareholders not to patients. Until pricing regulation catches up, AI in drug development is a margin story not an access story. Important distinction for policymakers and anyone following healthcare AI trends.
ClinTrials_Rosa
Clinical Research Associate · CRO
AI in clinical trials is where I see the most immediate impact tbh. Patient recruitment using AI to match EHR (Electronic Health Records) data to eligibility criteria is cutting enrollment time by up to 50% in some trials I've worked on. We also use NLP (Natural Language Processing) to screen clinical notes — finds eligible patients that manual screening misses. Dropout prediction is another one, AI flags patients at risk of dropping out so we can intervene. These arent glamorous headlines but theyre real operational improvements.
VetNarayanan_ICMR
Researcher · ICMR affiliated institute
From an Indian research perspective — we're quite behind on AI drug discovery infrastructure. ICMR and DBT have started funding AI-pharma collaboration but the data silos between institutions are brutal. You cant build good ML models for drug discovery without large clean datasets and Indian hospitals just dont share data easily — privacy concerns, legal ambiguity, institutional politics. The National Health Data Management Policy is supposed to fix this but implementation is still very patchy. Huge opportunity being wasted rn.
