| Title |
AI-Driven investment and startup success: data analytics for scaling deep tech ventures in life science sector |
| Translation of Title |
Dirbtiniu intelektu grįstos investicijos ir startuolių sėkmė: duomenų analitika aukštųjų technologijų verslų plėtrai gyvybės mokslų sektoriuje. |
| Authors |
Aalam, Hamza |
| Full Text |
|
| Pages |
87 |
| Keywords [eng] |
Artificial Intelligence (AI), biotech startups, deep tech ventures, life sciences, data analytics, predictive modeling, drug discovery, investment strategy, startup scaling, personalized medi-cine, genomics, synthetic biology, natural language processing (NLP), machine learning, clin-ical trials, investor confidence, entrepreneurial innovation |
| Abstract [eng] |
Master’s thesis was prepared in Vilnius, in 2026 Scope of Master’s thesis – 61 pages. Number of tables used in the FMTP - 9pcs. Number of figures used in the FMTP – 5 pcs. Number of bibliography and references - 65pcs. The FMTP described in brief: Biotech startups operate in a highly uncertain, capital-intensive, and regulated environment, which makes investment decision-making and scaling particularly challenging. Recent ad vances in Artificial Intelligence (AI) and data analytics have introduced new possibilities to accelerate research processes, reduce risk, and improve transparency for investors in the life science sector. Problem statement: Biotech startups face difficulties in attracting investment and scaling efficiently due to long development cycles, high scientific and regulatory risks, and limited visibility of future out comes. Traditional evaluation methods are often insufficient to assess early-stage biotech ventures, increasing uncertainty for investors. Objective and tasks of the FMTP: The objective of this master’s thesis is to explore how AI driven data analytics can support investment readiness and scalable growth in biotech startups within the life science sector Specific objectives: 4 - - - - To review scientific literature on AI use in biotech innovation and investment readiness. To examine how AI is applied in biotech startups to improve research and decision making processes. To analyze investor perceptions of AI as a tool for risk reduction and value assessment To evaluate the role of AI in supporting startup scalability through empirical evidence Research methods used in the FMTP: The research is based on a qualitative approach Semi structured interviews were conducted with biotech founders investors and AI specialists In addition 5 case studies of AI driven bio tech startups were analysed The collected data were examined using thematic and cross case analysis . Research and results obtained: The study used interviews and case studies to analyze how AI affects research efficiency, investment decisions, and scalability in biotech startups. The results show that AI improves productivity, reduces risk, and increases investor confidence. Conclusions of the FMTP: The FMTP concludes that AI acts as a strategic enabler that enhances investment readiness and sustainable growth in biotech ventures. Its effectiveness depends on ethical use, data quality, and human oversight. Information about the publication of FMTP results or adaptation for publication: The results are suitable for adaptation into an academic or professional publication on AI driven innovation and investment in life sciences. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2026 |
