Valuation: Exscientia plc

Market Cap 500M 667M 583M 548M 938M 63.75B 938M 6.57B 2.54B 32.56B 2.5B 2.45B 105B P/E 2022
-4.55x
P/E 2023 -4.28x
Enterprise Value 156M 208M 182M 171M 292M 19.86B 292M 2.05B 790M 10.14B 780M 763M 32.7B EV / Sales 2022
1.9x
EV / Sales 2023 14.2x
Free-Float
36.87%
Yield 2022 *
-
Yield 2023 -
3 years 3.8
Extreme 3.8
7.91
5 years 3.8
Extreme 3.8
30.38
10 years 3.8
Extreme 3.8
30.38
Manager TitleAgeSince
Chief Tech/Sci/R&D Officer 61 06/06/2024
Chief Tech/Sci/R&D Officer - 01/05/2022
Chief Tech/Sci/R&D Officer - 01/05/2024
Director TitleAgeSince
Director/Board Member 63 28/09/2017
Director/Board Member 43 01/05/2020
Chairman 61 12/02/2024
Change 5-day change 1-year change 3-year change Capi.($)
+3.20%-.--%-.--%-1.02% 633M
+2.55%+5.35%+3.61%+32.02% 46.68B
+0.08%+1.66%+44.26%+35.60% 44.42B
+0.05%-5.97%+342.95%+532.74% 41.32B
+0.66%+4.03%+10.07%+96.02% 41.58B
-1.60%-0.17%+9.73%+43.63% 30.14B
+0.96%+5.84%+26.39%+161.70% 17.42B
-0.01%-10.32% - - 15.6B
+1.15%+9.42%+26.21%+42.84% 15.54B
-1.08%+24.08%-12.52% - 14.08B
Average +0.57%+3.05%+50.08%+117.94% 26.74B
Weighted average by Cap. +0.43%+1.94%+70.32%+142.50%

Financials

2022 2023
Net sales 27.22M 36.32M 31.74M 29.83M 51.09M 3.47B 51.1M 358M 138M 1.77B 136M 133M 5.72B 20.08M 26.79M 23.41M 22M 37.68M 2.56B 37.69M 264M 102M 1.31B 101M 98.4M 4.22B
Net income -119M -158M -138M -130M -223M -15.14B -223M -1.56B -603M -7.73B -595M -582M -24.93B -146M -195M -170M -160M -274M -18.62B -274M -1.92B -741M -9.51B -731M -715M -30.65B
Net Debt -492M -656M -574M -539M -923M -62.74B -923M -6.47B -2.5B -32.04B -2.46B -2.41B -103B -344M -459M -401M -377M -646M -43.89B -646M -4.53B -1.75B -22.41B -1.72B -1.69B -72.26B
Logo Exscientia plc
Exscientia PLC is a drug design and development company. The Company combines precision design with integrated experimentation to invent and develop drugs. It uses artificial intelligence (AI) in drug discovery to progress AI-designed small molecules into a clinical setting. It uses the patient's tissue data to define optimal profiles for research, improve experimental assessment during design and improve outcomes in a medical setting. It has developed an internal pipeline focused on oncology, while its partnered pipeline extends to various other therapeutic areas. It combines genetic data and global literature in machine learning models to anticipate and confirm disease-target associations. The Company's experimental platform records responses in real patient samples allowing it to generate high-precision views of potential patient response. Its product pipeline consists of GTAEXS617, EXS4318, EXS74539, and EXS73565.
Employees
483
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