Valuation: Exscientia plc

Market Cap 500M 674M 584M 544M 945M 64.25B 959M 6.41B 2.52B 32.02B 2.53B 2.47B 106B P/E 2022
-4.55x
P/E 2023 -4.28x
Enterprise Value 156M 210M 182M 169M 294M 20.02B 299M 2B 785M 9.97B 788M 771M 33.05B 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
9.12
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 05/06/2024
Chief Tech/Sci/R&D Officer - 30/04/2022
Chief Tech/Sci/R&D Officer - 30/04/2024
Director TitleAgeSince
Director/Board Member 63 27/09/2017
Director/Board Member 43 30/04/2020
Chairman 61 11/02/2024
Change 5-day change 1-year change 3-year change Capi.($)
+3.20%-.--%-.--%-37.47% 633M
-1.40%+3.69%-2.09%+11.41% 49.37B
-1.16%+13.36%+22.70%+3.92% 39.14B
-2.41%-0.65%+406.63%+629.40% 40.85B
-1.88%-3.29%+5.36%+40.37% 35.81B
-1.74%+8.42%+8.85%+48.87% 30.5B
-2.85%-3.46% - - 16.55B
-0.21%-4.74%+31.83%+126.49% 16.14B
-1.19%-3.97%-9.88%-35.13% 12.77B
-0.64%+3.09%+3.15%+9.37% 13.71B
Average -1.03%+5.20%+51.84%+88.58% 25.55B
Weighted average by Cap. -1.59%+6.70%+76.55%+130.01%

Financials

2022 2023
Net sales 27.22M 36.7M 31.82M 29.63M 51.45M 3.5B 52.26M 349M 137M 1.74B 138M 135M 5.78B 20.08M 27.07M 23.47M 21.85M 37.95M 2.58B 38.54M 258M 101M 1.29B 102M 99.41M 4.26B
Net income -119M -160M -139M -129M -224M -15.26B -228M -1.52B -598M -7.6B -601M -588M -25.2B -146M -197M -171M -159M -276M -18.76B -280M -1.87B -735M -9.35B -738M -723M -30.98B
Net Debt -492M -663M -575M -535M -930M -63.23B -944M -6.31B -2.48B -31.51B -2.49B -2.44B -104B -344M -464M -402M -375M -650M -44.24B -661M -4.41B -1.73B -22.04B -1.74B -1.7B -73.04B
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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